1. Learning invariant object recognition from temporal correlation in a hierarchical network
Author(s): Markus Lessmann, Rolf P. Würtz
2. Impulsive synchronization schemes of stochastic complex networks with switching topology: Average time approach
Author(s): Chaojie Li, Wenwu Yu, Tingwen Huang
Friday, March 21, 2014
Thursday, March 20, 2014
Finding an academic job
Finding a job for any profession is difficult, but finding a job in
academia can be ridiculously hard. Since I finished my PhD ten years
ago, I've had two periods of unemployment, totalling more than six
months out of work. Since I was the sole income-earner for my family at
the time, those periods were particularly difficult and stressful to get
through, but I got through them, as much by luck as by design.
I've come across a collection of articles on finding academic jobs, that I wanted to share and comment on. I tend to agree with what most of them have to say, even though they're not specific to computational intelligence in particular or even computer science or engineering in general.
The first discusses whether you should even go for an academic job. For computer scientists it is probably easier to go into industry with a PhD than it is for other PhD graduates. The tech industry is always strong somewhere, and it is always growing, so there are always jobs to be had. In my own country of New Zealand, at any one time there are usually between two and three thousand vacancies in the IT sector, and that's out of a country of around four million.
The second article discusses ways to improve your chances of getting an academic job. The authors mention engaging with the community, publishing papers and emphasising transferable skills. I've actually had one supervisor tell me that there is nothing more important than publishing papers, while this article argues that too much publishing runs the risk of establishing nothing more than an unfocussed research record, and this article argues that teaching experience, including experience designing and administering courses, is very important for getting an academic job. Even though I was more interested in the research side of things during my Honours year, I still worked as a tutor for a database course, and during my post-grad years I tutored computational intelligence courses. Near the end of my studies, I worked full-time as a teaching fellow (I believe in the USA that would be Teaching Assistant, but I can't be sure) and did re-work and administer courses. I am quite certain that this experience helped me to get the job I have now.
Having decided to stay in academia, and done the ground-work to enter the academic profession, the next step is to find an academic job. When you've identified a job you are interested in, the first thing you must do it get your academic CV in order. There are several common mistakes you must avoid in this document, since one mistake is all it takes for a recruiter (who are not academics) to discard your entire application. The previous two articles have some very good pieces of advice for laying out your CV, and I have applied several of them to improve my own CV.
Things like career objective statements should also be left out of a CV. My current job means that I regularly receive unsolicited emails complete with CV from people wanting a teaching job in my department, and most of them have things like career objective statements that have nothing whatsoever to do with my department or any kind of teaching job. Nothing says "desperate blanket bombing" like failing to do even a minimum of research about the place you are sending your job application to.
The other major component of an application for an academic job is the cover letter. This should also be specific for the position you are applying for, it should cover all of the criteria mentioned in the job advertisement, and it should be short. If you make it too long and detailed, then you run the risk of boring the recruiter before they finish reading, which usually results in your application being thrown away.
If you have a compelling CV, and have written a very good cover letter that shows that you are very well suited to the job, then you might get an interview. This article talks about how to prepare for an interview. One of the points in it that I would emphasise is the need to do your research before the interview. One of the fundamental rules in the Art of War is "Know yourself, and know others, and you shall have one hundred victories in one hundred battles". This applies to interviews as well! Know who is going to be interviewing you: have they published with anyone you know? Is there any other connection? What relationship does their research have to yours? This article also has some tips on how to handle tricky interview questions. Some questions just can't be answered well, like the question I got once about how I demonstrated an awareness of diversity in the classroom (I'm from the whitest district in New Zealand and I married a Chinese, I think that shows a pretty good awareness of diversity). Obviously, some self-confidence is very important, and I've been lucky in that a couple of times some really good people have boosted my self-esteem just before interviews.
Usually, by the end of an interview, I know whether I've gotten the job or not, just by the way the interview went. If I have struggled with any of the questions, then I probably won't get it. If it's gone smoothly, then I know I've got a much better chance. There have been a couple of cases where the interview went well and I still didn't get the job, but those were years ago and for positions that were probably above my skill level at the time.
Job hunting is brutal, and academic positions invariably attract a lot of applications (especially New Zealand positions, as for some reason a lot of people want to move here). The last time I was out of work, I sent off two dozen applications, which resulted in three interviews, which led to two job offers. And that was with a PhD, four years teaching experience, almost five years post-doc experience, and more than forty publications. But, if you stick to it, you will find a job. It might not be the job you first had in mind when you started, but it will be just as good, and any job is good experience if you're clever about how you do it.
I've come across a collection of articles on finding academic jobs, that I wanted to share and comment on. I tend to agree with what most of them have to say, even though they're not specific to computational intelligence in particular or even computer science or engineering in general.
The first discusses whether you should even go for an academic job. For computer scientists it is probably easier to go into industry with a PhD than it is for other PhD graduates. The tech industry is always strong somewhere, and it is always growing, so there are always jobs to be had. In my own country of New Zealand, at any one time there are usually between two and three thousand vacancies in the IT sector, and that's out of a country of around four million.
The second article discusses ways to improve your chances of getting an academic job. The authors mention engaging with the community, publishing papers and emphasising transferable skills. I've actually had one supervisor tell me that there is nothing more important than publishing papers, while this article argues that too much publishing runs the risk of establishing nothing more than an unfocussed research record, and this article argues that teaching experience, including experience designing and administering courses, is very important for getting an academic job. Even though I was more interested in the research side of things during my Honours year, I still worked as a tutor for a database course, and during my post-grad years I tutored computational intelligence courses. Near the end of my studies, I worked full-time as a teaching fellow (I believe in the USA that would be Teaching Assistant, but I can't be sure) and did re-work and administer courses. I am quite certain that this experience helped me to get the job I have now.
Having decided to stay in academia, and done the ground-work to enter the academic profession, the next step is to find an academic job. When you've identified a job you are interested in, the first thing you must do it get your academic CV in order. There are several common mistakes you must avoid in this document, since one mistake is all it takes for a recruiter (who are not academics) to discard your entire application. The previous two articles have some very good pieces of advice for laying out your CV, and I have applied several of them to improve my own CV.
Things like career objective statements should also be left out of a CV. My current job means that I regularly receive unsolicited emails complete with CV from people wanting a teaching job in my department, and most of them have things like career objective statements that have nothing whatsoever to do with my department or any kind of teaching job. Nothing says "desperate blanket bombing" like failing to do even a minimum of research about the place you are sending your job application to.
The other major component of an application for an academic job is the cover letter. This should also be specific for the position you are applying for, it should cover all of the criteria mentioned in the job advertisement, and it should be short. If you make it too long and detailed, then you run the risk of boring the recruiter before they finish reading, which usually results in your application being thrown away.
If you have a compelling CV, and have written a very good cover letter that shows that you are very well suited to the job, then you might get an interview. This article talks about how to prepare for an interview. One of the points in it that I would emphasise is the need to do your research before the interview. One of the fundamental rules in the Art of War is "Know yourself, and know others, and you shall have one hundred victories in one hundred battles". This applies to interviews as well! Know who is going to be interviewing you: have they published with anyone you know? Is there any other connection? What relationship does their research have to yours? This article also has some tips on how to handle tricky interview questions. Some questions just can't be answered well, like the question I got once about how I demonstrated an awareness of diversity in the classroom (I'm from the whitest district in New Zealand and I married a Chinese, I think that shows a pretty good awareness of diversity). Obviously, some self-confidence is very important, and I've been lucky in that a couple of times some really good people have boosted my self-esteem just before interviews.
Usually, by the end of an interview, I know whether I've gotten the job or not, just by the way the interview went. If I have struggled with any of the questions, then I probably won't get it. If it's gone smoothly, then I know I've got a much better chance. There have been a couple of cases where the interview went well and I still didn't get the job, but those were years ago and for positions that were probably above my skill level at the time.
Job hunting is brutal, and academic positions invariably attract a lot of applications (especially New Zealand positions, as for some reason a lot of people want to move here). The last time I was out of work, I sent off two dozen applications, which resulted in three interviews, which led to two job offers. And that was with a PhD, four years teaching experience, almost five years post-doc experience, and more than forty publications. But, if you stick to it, you will find a job. It might not be the job you first had in mind when you started, but it will be just as good, and any job is good experience if you're clever about how you do it.
Wednesday, March 19, 2014
IEEE Transactions on Neural Networks and Learning Systems Volume 25, Number 4, April 2014
REGULAR PAPERS
1. Dynamic Uncertain Causality Graph for Knowledge Representation and Probabilistic Reasoning: Statistics Base, Matrix, and ApplicationAuthor(s): Q. Zhang, C. Dong, Y. Cui, and Z. Yang
Pages: 645-663
2. T2FELA: Type-2 Fuzzy Extreme Learning Algorithm for Fast Training of Interval Type-2 TSK Fuzzy Logic System
Author(s): Z. Deng, K.-S. Choi, L. Cao, and S. Wang
Pages: 664-676
3. Adaptive Quasi-Newton Algorithm for Source Extraction via CCA Approach
Author(s): W.-T. Zhang, S.-T. Lou, and D.-Z. Feng
Pages: 677-689
4. Lagrange Stability of Memristive Neural Networks With Discrete and Distributed Delays
Author(s): A. Wu and Z. Zeng
Pages: 690-703
5. Attractivity Analysis of Memristor-Based Cellular Neural Networks With Time-Varying Delays
Author(s): Z. Guo, J. Wang, and Z. Yan
Pages: 704-717
6. Novel Neural Control for a Class of Uncertain Pure-Feedback Systems
Author(s): Q. Shen, P. Shi, T. Zhang, and C.-C. Lim
Pages: 718-727
7. An Ordered-Patch-Based Image Classification Approach on the Image Grassmannian Manifold
Author(s): C. Xu, T. Wang, J. Gao, S. Cao, W. Tao, and F. Liu
Pages: 728-737
8. Artificial Neural Networks for Control of a Grid-Connected Rectifier/Inverter Under Disturbance, Dynamic and Power Converter Switching Conditions
Author(s): S. Li, M. Fairbank, C. Johnson, D. C. Wunsch, E. Alonso, and J. L. Proaño
Pages: 738-750
9. A Stochastic Mean Field Model for an Excitatory and Inhibitory Synaptic Drive Cortical Neuronal Network
Author(s): Q. Hui, W. M. Haddad, J. M. Bailey, and T. Hayakawa
Pages: 751-763
10. RandomBoost: Simplified Multiclass Boosting Through Randomization
Author(s): S. Paisitkriangkrai, C. Shen, Q. Shi, and A. van den Hengel
Pages: 764-779
11. A Unified Learning Framework for Single Image Super-Resolution
Author(s): J. Yu, X. Gao, D. Tao, X. Li, and K. Zhang
Pages: 780-792
12. L1-Norm Kernel Discriminant Analysis via Bayes Error Bound Optimization for Robust Feature Extraction
Author(s): W. Zheng, Z. Lin, and H. Wang
Pages: 793-805
BRIEF PAPERS
13. Online Motor Fault Detection and Diagnosis Using a Hybrid FMM-CART ModelAuthor(s): M. Seera and C. P. Lim
Pages: 806-811
14. Feature-Based Ordering Algorithm for Data Presentation of Fuzzy ARTMAP Ensembles
Author(s): T. H. Oong and N. A. M. Isa
Pages: 812-818
16. Self-Organization in Autonomous, Recurrent, Firing-Rate CrossNets With Quasi-Hebbian Plasticity
Author(s): T. J. Walls and K. K. Likharev
Pages: 819-823
17. A Recurrent Neural Network for Solving Bilevel Linear Programming Problem
Author(s): X. He, C. Li, T. Huang, C. Li, and J. Huang
Pages: 824-829
18. Local Stability Analysis of Discrete-Time, Continuous-State, Complex-Valued Recurrent Neural Networks With Inner State Feedback
Author(s): M. Mostafa, W. G. Teich, and J. Lindner
Pages: 830-842
19. Sparse Bayesian Extreme Learning Machine for Multi-classification
Author(s): J. Luo, C.-M. Vong, and P.-K. Wong
Pages: 843
Tuesday, March 18, 2014
IEEE Transactions on Autonomous Mental Development, Volume 6, Number 1, March 2014
1. An Approach to Subjective Computing: A Robot That Learns From Interaction With Humans
Author(s): P. Grüneberg and K. Suzuki
Pages: 5-18
2. LIDA: A Systems-level Architecture for Cognition, Emotion, and Learning
Author(s): S. Franklin, T. Madl, S. D’Mello, and J. Snaider
Pages: 19-41
3. Development of First Social Referencing Skills: Emotional Interaction as a Way to Regulate Robot Behavior
Author(s): S. Boucenna, P. Gaussier, and L. Hafemeister
Pages: 42-55
4. Object Learning Through Active Exploration
Author(s): S. Ivaldi, S. M. Nguyen, N. Lyubova, A. Droniou, V. Padois, D. Filliat, P.-Y. Oudeyer, and O. Sigaud
Pages: 56-72
5. Erratum to "Modeling Cross-Modal Interactions in EarlyWord Learning"
Pages: 73
Author(s): P. Grüneberg and K. Suzuki
Pages: 5-18
2. LIDA: A Systems-level Architecture for Cognition, Emotion, and Learning
Author(s): S. Franklin, T. Madl, S. D’Mello, and J. Snaider
Pages: 19-41
3. Development of First Social Referencing Skills: Emotional Interaction as a Way to Regulate Robot Behavior
Author(s): S. Boucenna, P. Gaussier, and L. Hafemeister
Pages: 42-55
4. Object Learning Through Active Exploration
Author(s): S. Ivaldi, S. M. Nguyen, N. Lyubova, A. Droniou, V. Padois, D. Filliat, P.-Y. Oudeyer, and O. Sigaud
Pages: 56-72
5. Erratum to "Modeling Cross-Modal Interactions in EarlyWord Learning"
Pages: 73
Monday, March 17, 2014
IEEE Transactions on Computational Intelligence and AI in Games - Volume 6, Number 1, March 2014
1. General Self-Motivation and Strategy Identification: Case Studies Based on Sokoban and Pac-Man
Author(s): T. Anthony, D. Polani, and C. L. Nehaniv
Pages: 1-17
2. Passing a Hide-and-Seek Third-Person Turing Test
Author(s): A. Cenkner, V. Bulitko, M. Spetch, E. Legge, C. G. Anderson, and M. Brown
Pages: 18-30
3. Solving the Physical Traveling Salesman Problem: Tree Search and Macro Actions
Author(s): D. Perez, E. J. Powley, D. Whitehouse, P. Rohlfshagen, S. Samothrakis, P. I. Cowling, and S. M. Lucas
Pages: 31-45
4. Two Online Learning Playout Policies in Monte Carlo Go: An Application of Win/Loss States
Author(s): J. Basaldúa, S. Stewart, J. M. Moreno-Vega, and P. D. Drake
Pages: 46-54
5. DeepQA Jeopardy! Gamification: A Machine-Learning Perspective
Author(s): A. K. Baughman, W. Chuang, K. R. Dixon, Z. Benz, and J. Basilico
Pages: 55-66
6. A Micromanagement Task Allocation System for Real-Time Strategy Games
Author(s): K. D. Rogers and A. A. Skabar
Pages: 67-77
7. Procedural Generation of Dungeons
Author(s): R. van der Linden, R. Lopes, and R. Bidarra
Pages: 78-89
Author(s): T. Anthony, D. Polani, and C. L. Nehaniv
Pages: 1-17
2. Passing a Hide-and-Seek Third-Person Turing Test
Author(s): A. Cenkner, V. Bulitko, M. Spetch, E. Legge, C. G. Anderson, and M. Brown
Pages: 18-30
3. Solving the Physical Traveling Salesman Problem: Tree Search and Macro Actions
Author(s): D. Perez, E. J. Powley, D. Whitehouse, P. Rohlfshagen, S. Samothrakis, P. I. Cowling, and S. M. Lucas
Pages: 31-45
4. Two Online Learning Playout Policies in Monte Carlo Go: An Application of Win/Loss States
Author(s): J. Basaldúa, S. Stewart, J. M. Moreno-Vega, and P. D. Drake
Pages: 46-54
5. DeepQA Jeopardy! Gamification: A Machine-Learning Perspective
Author(s): A. K. Baughman, W. Chuang, K. R. Dixon, Z. Benz, and J. Basilico
Pages: 55-66
6. A Micromanagement Task Allocation System for Real-Time Strategy Games
Author(s): K. D. Rogers and A. A. Skabar
Pages: 67-77
7. Procedural Generation of Dungeons
Author(s): R. van der Linden, R. Lopes, and R. Bidarra
Pages: 78-89
Friday, March 14, 2014
Reminder: paper submission deadline for IEEE SSCI 2014
A reminder that the deadline for submitting papers to the IEEE Symposium Series on Computational Intelligence (SSCI) 2014 is 15 June 2014. This group of symposia will be held in Orlando, Florida, 9-12 December, 2014.
Tuesday, March 11, 2014
Neural Networks new articles 4 March - 10 March
1. Stable locality sensitive discriminant analysis for image recognition
Author(s): Quanxue Gao, Jingjing Liu, Kai Cui, Hailin Zhang, Xiaogang Wang
2. Growing Neural Gas approach for obtaining homogeneous maps by restricting the insertion of new nodes
Author(s): Yuri Quintana-Pacheco, Daniel Ruiz-Fernández, Agustín Magrans-Rico
3. Global asymptotic stability analysis for delayed neural networks using a matrix-based quadratic convex approach
Author(s): Xian-Ming Zhang, Qing-Long Han
Author(s): Quanxue Gao, Jingjing Liu, Kai Cui, Hailin Zhang, Xiaogang Wang
2. Growing Neural Gas approach for obtaining homogeneous maps by restricting the insertion of new nodes
Author(s): Yuri Quintana-Pacheco, Daniel Ruiz-Fernández, Agustín Magrans-Rico
3. Global asymptotic stability analysis for delayed neural networks using a matrix-based quadratic convex approach
Author(s): Xian-Ming Zhang, Qing-Long Han
Friday, March 7, 2014
IEEE SMC 2014 Special Session: Autonomous Learning and Evolving Intelligence
Below is a call for papers for a special session in the 2014 IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC). This conference will be held in San Diego, California, October 5-8, 2014.
To achieve autonomous learning, a computer develops a form of intelligence that can evolve and adapt to its surroundings. A system that learns and evolves automatically should also operate in real-time. Currently DARPA have a challenge running for robots to operate autonomously in inhospitable environments; NSF is also recognising autonomous learning as a significant topic of research; large companies such as IBM, BT etc. also have programmes in autonomic computing and related disciplines. The objective of the proposed special session is to bring together people from academia and industry to introduce papers that look at addressing some of the fundamental problems or stumbling blocks found when a computer must learn for itself and evolve to it's surroundings.
University of Lancaster, UK
Submission of a full-length paper
May 25, 2014
Acceptance/Rejection notification
July 9, 2014
Final camera-ready paper submission
Autonomous Learning and Evolving Intelligence
Synopsis
The special session is focussed on addressing autonomous learning in computational systems in a setting where the role of the human is to merely to start/stop the process and monitor it online. It is a specific branch of machine learning, where the computer system is expected to learn for itself within a dynamically evolving and challenging environment complex processes without heuristic input or prior training.To achieve autonomous learning, a computer develops a form of intelligence that can evolve and adapt to its surroundings. A system that learns and evolves automatically should also operate in real-time. Currently DARPA have a challenge running for robots to operate autonomously in inhospitable environments; NSF is also recognising autonomous learning as a significant topic of research; large companies such as IBM, BT etc. also have programmes in autonomic computing and related disciplines. The objective of the proposed special session is to bring together people from academia and industry to introduce papers that look at addressing some of the fundamental problems or stumbling blocks found when a computer must learn for itself and evolve to it's surroundings.
Indicative Topics /Areas (not limited to)
- Autonomous Learning
- Autonomous Video Analytics
- Intelligence and Adaptive Systems
- Adaptive and Self-calibrating Sensor Systems
- Autonomous Fuzzy rule-based Systems
- Anomaly Detection
- Fault Detection and Identification
- Evolving Clustering
- Evolving Classification Methods
- Adaptive Behaviour Models
- Robotic Systems
Submission details
Papers should not exceed 8 pages in length, papers over 6 pages in length are charged extra per page (up to a max of 2). Manuscript for a Special Session should NOT be submitted in duplication to any other regular or special session and should be submitted to SMC 2014 main conference online submission system on SMC 2014 conference website. All submitted papers of Special Session have to undergo the same review process (a t least two reviewers). The technical reviewers for each Special Session paper will be members of the SMC 2014 Program Committee and qualified peer-reviewers to be nominated by the Special Session organizers.Special Session organizer
Plamen AngelovUniversity of Lancaster, UK
Important Dates
April 7, 2014Submission of a full-length paper
May 25, 2014
Acceptance/Rejection notification
July 9, 2014
Final camera-ready paper submission
Thursday, March 6, 2014
Conference paper deadline: WCCS 14
The deadline for submitting abstracts to the World Congress on Complex Systems (WCCS) 2014 is 15 May, 2014. This conference will be held in Agadir, Morocco, November 10-14, 2014.
Wednesday, March 5, 2014
Neural Networks new articles 24 February - 2 March
1. An improved robust stability result for uncertain neural networks with multiple time delays
Author(s): Sabri Arik
2. Necessary and sufficient condition for multistability of neural networks evolving on a closed hypercube
Author(s): Mauro Di Marco, Mauro Forti, Massimo Grazzini, Luca Pancioni
3. Interaction of feedforward and feedback streams in visual cortex in a firing-rate model of columnar computations
Author(s): Tobias Brosch, Heiko Neumann
4. Solving the linear interval tolerance problem for weight initialization of neural networks
Author(s): S.P. Adam, D.A. Karras, G.D. Magoulas, M.N. Vrahatis
5. Further results on robustness analysis of global exponential stability of recurrent neural networks with time delays and random disturbances
Author(s): Weiwei Luo, Kai Zhong, Song Zhu, Yi Shen
Author(s): Sabri Arik
2. Necessary and sufficient condition for multistability of neural networks evolving on a closed hypercube
Author(s): Mauro Di Marco, Mauro Forti, Massimo Grazzini, Luca Pancioni
3. Interaction of feedforward and feedback streams in visual cortex in a firing-rate model of columnar computations
Author(s): Tobias Brosch, Heiko Neumann
4. Solving the linear interval tolerance problem for weight initialization of neural networks
Author(s): S.P. Adam, D.A. Karras, G.D. Magoulas, M.N. Vrahatis
5. Further results on robustness analysis of global exponential stability of recurrent neural networks with time delays and random disturbances
Author(s): Weiwei Luo, Kai Zhong, Song Zhu, Yi Shen
Tuesday, March 4, 2014
Evolving Systems Volume 5, Number 1, 2014
1. Editorial: Applications, results and future direction (EAIS 12)
Author(s): José Antonio Iglesias & Igor Škrjanc
2. A robust fuzzy adaptive law for evolving control systems
Author(s):Sašo Blažič , Igor Škrjanc & Drago Matko
3. Fault-tolerant gait learning and morphology optimization of a polymorphic walking robot
Author(s):David Johan Christensen , Jørgen Christian Larsen & Kasper Stoy
4. Elastic Adaptive Dynamics Methodology on Ontology Matching on Evolving Folksonomy Driven Environment
Author(s):Massimiliano Dal Mas
5. Dynamic learning in cognitive robotics through a procedural long term memory
Author(s):Francisco Bellas , Pilar Caamaño , Andrés Faiña & Richard J. Duro
6. Adaptive evolving strategy for dextrous robotic manipulation
Author(s):César Arismendi , David Álvarez , Santiago Garrido & Luis Moreno
Author(s): José Antonio Iglesias & Igor Škrjanc
2. A robust fuzzy adaptive law for evolving control systems
Author(s):Sašo Blažič , Igor Škrjanc & Drago Matko
3. Fault-tolerant gait learning and morphology optimization of a polymorphic walking robot
Author(s):David Johan Christensen , Jørgen Christian Larsen & Kasper Stoy
4. Elastic Adaptive Dynamics Methodology on Ontology Matching on Evolving Folksonomy Driven Environment
Author(s):Massimiliano Dal Mas
5. Dynamic learning in cognitive robotics through a procedural long term memory
Author(s):Francisco Bellas , Pilar Caamaño , Andrés Faiña & Richard J. Duro
6. Adaptive evolving strategy for dextrous robotic manipulation
Author(s):César Arismendi , David Álvarez , Santiago Garrido & Luis Moreno
Monday, March 3, 2014
Neural Networks, Volume 52, Pages 1-76, April 2014
1. Pairwise constrained concept factorization for data representation
Pages: 1-17
Author(s): Yangcheng He, Hongtao Lu, Lei Huang, Saining Xie
2. Hybrid extreme rotation forest
Pages: 33-42
Author(s): Borja Ayerdi, Manuel Graña
3. Policy oscillation is overshooting
Pages: 43-61
Author(s): Paul Wagner
4. NeuCube: A spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data
Pages: 62-76
Author(s): Nikola K. Kasabov
5. Construction of a Boolean model of gene and protein regulatory network with memory
Pages: 18-24
Author(s): Meng Yang, Rui Li, Tianguang Chu
6. Nonsmooth finite-time stabilization of neural networks with discontinuous activations
Pages: 25-32
Author(s): Xiaoyang Liu, Ju H. Park, Nan Jiang, Jinde Cao
Pages: 1-17
Author(s): Yangcheng He, Hongtao Lu, Lei Huang, Saining Xie
2. Hybrid extreme rotation forest
Pages: 33-42
Author(s): Borja Ayerdi, Manuel Graña
3. Policy oscillation is overshooting
Pages: 43-61
Author(s): Paul Wagner
4. NeuCube: A spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data
Pages: 62-76
Author(s): Nikola K. Kasabov
5. Construction of a Boolean model of gene and protein regulatory network with memory
Pages: 18-24
Author(s): Meng Yang, Rui Li, Tianguang Chu
6. Nonsmooth finite-time stabilization of neural networks with discontinuous activations
Pages: 25-32
Author(s): Xiaoyang Liu, Ju H. Park, Nan Jiang, Jinde Cao
Tuesday, February 25, 2014
Neural Networks new articles 17 February - 23 February
1. Fastest strategy to achieve given number of neuronal firing in theta model
Author(s): Jiaoyan Wang, Qingyun Wang, Guanrong Chen
2. Matrix measure strategies for stability and synchronization of inertial BAM neural network with time delays
Author(s): Jinde Cao, Ying Wan
Author(s): Jiaoyan Wang, Qingyun Wang, Guanrong Chen
2. Matrix measure strategies for stability and synchronization of inertial BAM neural network with time delays
Author(s): Jinde Cao, Ying Wan
Friday, February 21, 2014
IEEE Transactions on Neural Networks and Learning Systems: Volume 25, Issue 3, March 2014
1. A Survey on CPG-Inspired Control Models and System Implementation
Author(s): Junzhi Yu; Min Tan; Jian Chen; Jianwei Zhang
Pages: 441 - 456
2. Robust Model Predictive Control of Nonlinear Systems With Unmodeled Dynamics and Bounded Uncertainties Based on Neural Networks
Author(s): Zheng Yan; Jun Wang
Pages: 457 - 469
3. Multi-Level Fuzzy Min-Max Neural Network Classifier
Author(s): Reza Davtalab; Mir Hossein Dezfoulian; Muharram Mansoorizadeh
Pages: 470 - 482
4. Adaptive Identifier for Uncertain Complex Nonlinear Systems Based on Continuous Neural Networks
Author(s): Mariel Alfaro-Ponce; Amadeo Arguelles Cruz; Isaac Chairez
Pages: 483 - 494
5. Function Approximation Using Combined Unsupervised and Supervised Learning
Author(s): Peter Andras
Pages: 495 - 505
6. Active Learning of Pareto Fronts
Author(s): Paolo Campigotto; Andrea Passerini; Roberto Battiti
Pages: 506 - 519
7. Learning Harmonium Models With Infinite Latent Features
Author(s): Ning Chen; Jun Zhu; Fuchun Sun; Bo Zhang
Pages: 520 - 532
8. A Class of Quaternion Kalman Filters
Author(s): Cyrus Jahanchahi; Danilo P. Mandic
Pages: 533 - 544
9. Neural Network for Nonsmooth, Nonconvex Constrained Minimization Via Smooth Approximation
Author(s): Wei Bian; Xiaojun Chen
Pages: 545 - 556
10. Nonbinary Associative Memory With Exponential Pattern Retrieval Capacity and Iterative Learning
Author(s): Amir Hesam Salavati; K. Raj Kumar; Amin Shokrollahi
Pages: 557 - 570
11. A Constrained Backpropagation Approach for the Adaptive Solution of Partial Differential Equations
Author(s): Keith Rudd; Gianluca Di Muro; Silvia Ferrari
Pages: 571 - 584
12. A Robust and Scalable Neuromorphic Communication System by Combining Synaptic Time Multiplexing and MIMO-OFDM
Author(s): Narayan Srinivasa; Deying Zhang; Beayna Grigorian
Pages: 585 - 608
13. ERNN: A Biologically Inspired Feedforward Neural Network to Discriminate Emotion From EEG Signal
Author(s): Reza Khosrowabadi; Chai Quek; Kai Keng Ang; Abdul Wahab
Pages: 609 - 620
14. Policy Iteration Adaptive Dynamic Programming Algorithm for Discrete-Time Nonlinear Systems
Author(s): Derong Liu; Qinglai Wei
Pages: 621 - 634
15. Reinforcement Learning Output Feedback NN Control Using Deterministic Learning Technique
Author(s): Bin Xu; Chenguang Yang; Zhongke Shi
Pages: 635 - 641
Author(s): Junzhi Yu; Min Tan; Jian Chen; Jianwei Zhang
Pages: 441 - 456
2. Robust Model Predictive Control of Nonlinear Systems With Unmodeled Dynamics and Bounded Uncertainties Based on Neural Networks
Author(s): Zheng Yan; Jun Wang
Pages: 457 - 469
3. Multi-Level Fuzzy Min-Max Neural Network Classifier
Author(s): Reza Davtalab; Mir Hossein Dezfoulian; Muharram Mansoorizadeh
Pages: 470 - 482
4. Adaptive Identifier for Uncertain Complex Nonlinear Systems Based on Continuous Neural Networks
Author(s): Mariel Alfaro-Ponce; Amadeo Arguelles Cruz; Isaac Chairez
Pages: 483 - 494
5. Function Approximation Using Combined Unsupervised and Supervised Learning
Author(s): Peter Andras
Pages: 495 - 505
6. Active Learning of Pareto Fronts
Author(s): Paolo Campigotto; Andrea Passerini; Roberto Battiti
Pages: 506 - 519
7. Learning Harmonium Models With Infinite Latent Features
Author(s): Ning Chen; Jun Zhu; Fuchun Sun; Bo Zhang
Pages: 520 - 532
8. A Class of Quaternion Kalman Filters
Author(s): Cyrus Jahanchahi; Danilo P. Mandic
Pages: 533 - 544
9. Neural Network for Nonsmooth, Nonconvex Constrained Minimization Via Smooth Approximation
Author(s): Wei Bian; Xiaojun Chen
Pages: 545 - 556
10. Nonbinary Associative Memory With Exponential Pattern Retrieval Capacity and Iterative Learning
Author(s): Amir Hesam Salavati; K. Raj Kumar; Amin Shokrollahi
Pages: 557 - 570
11. A Constrained Backpropagation Approach for the Adaptive Solution of Partial Differential Equations
Author(s): Keith Rudd; Gianluca Di Muro; Silvia Ferrari
Pages: 571 - 584
12. A Robust and Scalable Neuromorphic Communication System by Combining Synaptic Time Multiplexing and MIMO-OFDM
Author(s): Narayan Srinivasa; Deying Zhang; Beayna Grigorian
Pages: 585 - 608
13. ERNN: A Biologically Inspired Feedforward Neural Network to Discriminate Emotion From EEG Signal
Author(s): Reza Khosrowabadi; Chai Quek; Kai Keng Ang; Abdul Wahab
Pages: 609 - 620
14. Policy Iteration Adaptive Dynamic Programming Algorithm for Discrete-Time Nonlinear Systems
Author(s): Derong Liu; Qinglai Wei
Pages: 621 - 634
15. Reinforcement Learning Output Feedback NN Control Using Deterministic Learning Technique
Author(s): Bin Xu; Chenguang Yang; Zhongke Shi
Pages: 635 - 641
Thursday, February 20, 2014
Conference papers
I've just finished reviewing a pile of papers for some upcoming conference: IJCNN 2014, and EAIS 2014. While these papers represent some good work, in some cases their presentation leaves a lot to be desired. After spending my post-doc career in ecology, I have come to the conclusion that authors in our community could learn something from the way papers are written in other sciences.
In the sciences, a paper has five-six sections: the abstract; introduction; methods; results; discussion; and sometimes conclusions. Each of these sections has a specific function, and these functions fit papers in computational intelligence just as well as other sciences. Now, computer science in general and computational intelligence in particular is fairly unusual among academic disciplines in that we write full papers for conferences, and give conference papers almost as much weight as journal articles. But this structure evolved to make the contents of papers easier to understand, so it is just as applicable to conference papers as it is to journal articles. The difference is that conference papers are mostly preliminary work, and are shorter, whereas journal articles are longer and report more complete work.
Firstly, the abstract. This is not just a slapped-on piece of text that kinda-sorta says what you did. The abstract is where you summarise the entire paper: what you did, why you did it, what you found. The abstract is the hook by which you draw the reader into your paper, so a bad abstract means people won't read (and cite!) your paper later on.
The introduction sets the scene for your paper: this is where you survey the relevant literature (including all of the introduction stuffing that seems to account for about half of most people's citation count), establish what the problem is, establish what has already been done, and say what you are going to do. If you have any hypotheses or research questions, this is where you lay them out. And every paper should be investigating some hypothesis or research question, even if you don't explicitly state it. The last part of the introduction is where you are setting out for your reader exactly what it is you are trying to achieve in your paper: the earlier parts of the introduction are where you set out why you're doing it.
The methods is where you describe what you did. If you are describing a new technique or algorithm, describe it here, then describe how you evaluated it. If you are using computational intelligence to approach a real-world problem, then describe how you did this. The methods should have enough detail that someone could replicate your work, if they had access to the same data as you did. Don't needlessly repeat well-known algorithms here: I'm quite sick of reviewing conference papers that describe a simple genetic algorithm in their methods section. I know how a simple genetic algorithm works, and so does 99% of the people who are likely to read that paper. If it's well-established, just say which algorithm you used and reference it, that's what references are for.
In the results section you report your results. Since this is a conference paper, you need to focus on the key results. Don't fill half a page or more with a table of numbers! Especially don't give your tables of results captions like "Table of results" - most of your audience will have at least a functional level of reading comprehension, and therefore will already know that they're results. The caption of a table should describe the contents of the table, especially what each column / row heading means, and what the numbers represent. Captions are supposed to be independent of the text, that is,a reader should be able to understand the contents of the table without having to read the entire paper. A large collection of numbers is hard to understand, so in a conference paper it is often better to use a graphical representation of the results than a table. The text of the results describe the results but does not interpret them, so results sections can be quite short. You can describe any analyses of results you did in the results section, but that should probably be left to the discussion.
The discussion in many ways mirrors the introduction, because you are interpreting your results in the context of the literature you cited in the introduction. You are also answering your research questions, identifying any potential shortcomings in your approach, and suggesting future lines of research.In other words, the discussion is where you bring together all of the other sections of your paper. A well-written discussion eliminates the need for a conclusion.
Write succinctly, don't spend a lot of time saying something that can be explained by a reference. When I started writing conference papers in the mid-late 90s, they were limited to four-six pages because conference proceedings were all on paper. Now, conference proceedings are on DVD the page limits tend to be longer, closer to eight pages. But this is the limit, not the recommended number of pages. It's like the speed limit on the roads, the speed limit is the fastest you are allowed to drive, not the minimum speed you should be driving at all times. Just as you adjust your driving speed to the conditions, you should adjust the length of your paper to the material you are presenting: it is better to produce a succinctly-written, clear and to-the-point four page conference paper than it is produce an eight page paper that covers the same work but buries the important points among pages of padding.
Every sentence in a conference papers needs to tell a story, if a sentence doesn't contribute something to the paper, take it out. Avoid common grammatical errors, and don't rely on a spell-checker. Spell-checkers only tell you if a word is incorrectly spelt, it doesn't tell you if it is the wrong word for that sentence (I've seen quite enough instances of a "pubic announcement", which sounds far ruder than what I assume they meant, which was a "public announcement"). Proof-read the paper at least twice, and if English is not your first language, for goodness sake get a native English speaker to read it. English grammar is bad enough for us native speakers, it has so many traps in it (especially with things like past / present / future tense) that errors are almost inevitable. Grammatical errors jar the reader out of the flow of the work, and if that happens often enough they will lose the thread of the paper and not understand what you are trying to communicate.
Researchers in computational intelligence can do good research, and are able to write good software. There is no reason they should not be able to write good conference papers.
In the sciences, a paper has five-six sections: the abstract; introduction; methods; results; discussion; and sometimes conclusions. Each of these sections has a specific function, and these functions fit papers in computational intelligence just as well as other sciences. Now, computer science in general and computational intelligence in particular is fairly unusual among academic disciplines in that we write full papers for conferences, and give conference papers almost as much weight as journal articles. But this structure evolved to make the contents of papers easier to understand, so it is just as applicable to conference papers as it is to journal articles. The difference is that conference papers are mostly preliminary work, and are shorter, whereas journal articles are longer and report more complete work.
Firstly, the abstract. This is not just a slapped-on piece of text that kinda-sorta says what you did. The abstract is where you summarise the entire paper: what you did, why you did it, what you found. The abstract is the hook by which you draw the reader into your paper, so a bad abstract means people won't read (and cite!) your paper later on.
The introduction sets the scene for your paper: this is where you survey the relevant literature (including all of the introduction stuffing that seems to account for about half of most people's citation count), establish what the problem is, establish what has already been done, and say what you are going to do. If you have any hypotheses or research questions, this is where you lay them out. And every paper should be investigating some hypothesis or research question, even if you don't explicitly state it. The last part of the introduction is where you are setting out for your reader exactly what it is you are trying to achieve in your paper: the earlier parts of the introduction are where you set out why you're doing it.
The methods is where you describe what you did. If you are describing a new technique or algorithm, describe it here, then describe how you evaluated it. If you are using computational intelligence to approach a real-world problem, then describe how you did this. The methods should have enough detail that someone could replicate your work, if they had access to the same data as you did. Don't needlessly repeat well-known algorithms here: I'm quite sick of reviewing conference papers that describe a simple genetic algorithm in their methods section. I know how a simple genetic algorithm works, and so does 99% of the people who are likely to read that paper. If it's well-established, just say which algorithm you used and reference it, that's what references are for.
In the results section you report your results. Since this is a conference paper, you need to focus on the key results. Don't fill half a page or more with a table of numbers! Especially don't give your tables of results captions like "Table of results" - most of your audience will have at least a functional level of reading comprehension, and therefore will already know that they're results. The caption of a table should describe the contents of the table, especially what each column / row heading means, and what the numbers represent. Captions are supposed to be independent of the text, that is,a reader should be able to understand the contents of the table without having to read the entire paper. A large collection of numbers is hard to understand, so in a conference paper it is often better to use a graphical representation of the results than a table. The text of the results describe the results but does not interpret them, so results sections can be quite short. You can describe any analyses of results you did in the results section, but that should probably be left to the discussion.
The discussion in many ways mirrors the introduction, because you are interpreting your results in the context of the literature you cited in the introduction. You are also answering your research questions, identifying any potential shortcomings in your approach, and suggesting future lines of research.In other words, the discussion is where you bring together all of the other sections of your paper. A well-written discussion eliminates the need for a conclusion.
Write succinctly, don't spend a lot of time saying something that can be explained by a reference. When I started writing conference papers in the mid-late 90s, they were limited to four-six pages because conference proceedings were all on paper. Now, conference proceedings are on DVD the page limits tend to be longer, closer to eight pages. But this is the limit, not the recommended number of pages. It's like the speed limit on the roads, the speed limit is the fastest you are allowed to drive, not the minimum speed you should be driving at all times. Just as you adjust your driving speed to the conditions, you should adjust the length of your paper to the material you are presenting: it is better to produce a succinctly-written, clear and to-the-point four page conference paper than it is produce an eight page paper that covers the same work but buries the important points among pages of padding.
Every sentence in a conference papers needs to tell a story, if a sentence doesn't contribute something to the paper, take it out. Avoid common grammatical errors, and don't rely on a spell-checker. Spell-checkers only tell you if a word is incorrectly spelt, it doesn't tell you if it is the wrong word for that sentence (I've seen quite enough instances of a "pubic announcement", which sounds far ruder than what I assume they meant, which was a "public announcement"). Proof-read the paper at least twice, and if English is not your first language, for goodness sake get a native English speaker to read it. English grammar is bad enough for us native speakers, it has so many traps in it (especially with things like past / present / future tense) that errors are almost inevitable. Grammatical errors jar the reader out of the flow of the work, and if that happens often enough they will lose the thread of the paper and not understand what you are trying to communicate.
Researchers in computational intelligence can do good research, and are able to write good software. There is no reason they should not be able to write good conference papers.
Monday, February 17, 2014
Neural Networks new articles 10 February-16 February
1. Multiple mu-stability of neural networks with unbounded time-varying delays
Author(s): Lili Wang, Tianping Chen
2. Extreme learning machine for ranking: Generalization analysis and applications
Author(s): Hong Chen, Jiangtao Peng, Yicong Zhou, Luoqing Li, Zhibin Pan
3. Learning using privileged information: SV M+ and weighted SVM
Author(s): Maksim Lapin, Matthias Hein, Bernt Schiele
4. Similarity preserving low-rank representation for enhanced data representation and effective subspace learning
Author(s): Zhao Zhang, Shuicheng Yan, Mingbo Zhao
Author(s): Lili Wang, Tianping Chen
2. Extreme learning machine for ranking: Generalization analysis and applications
Author(s): Hong Chen, Jiangtao Peng, Yicong Zhou, Luoqing Li, Zhibin Pan
3. Learning using privileged information: SV M+ and weighted SVM
Author(s): Maksim Lapin, Matthias Hein, Bernt Schiele
4. Similarity preserving low-rank representation for enhanced data representation and effective subspace learning
Author(s): Zhao Zhang, Shuicheng Yan, Mingbo Zhao
Thursday, February 6, 2014
The problem with academic journals 8
It's been a long time since I last blogged about the problems with academic journals. Several of my old posts described the behaviour of the giant academic publisher Elsevier, specifically trying to buy a law in the US Congress that would virtually ban researchers publishing in open-access journals. This resulted in an enormous backlash against Elsevier, including a boycott that now has more than 14,000 names, culminating in the proposed legislation being dropped.
Unfortunately, Elsevier is back to their old bad behaviour: they have been sending notices to researchers and academic network sites demanding the removal from the web of papers that the researchers' had published in Elsevier journals. While Elsevier may be within their legal rights to do so (since they demand that authors sign over copyright to Elsevier), preventing people from self-archiving papers that they wrote is highly
detrimental to science. In other words, Elsevier gets the research papers for free (submitted by the authors), they get the quality control for free (done by volunteer reviewers), and the administration of journals for free (done by volunteer editors). Then, they do some basic formatting and proof-reading, demand that the authors surrender all rights to the article, and publish it at an enormous profit.
Some publishers like the IEEE work the same way but allow for self-archiving, that is, they allow authors to post papers they have authored on their own websites for other researchers to access. The IEEE seems to be doing quite well out of this practice, but then the high offices of the IEEE are held by engineers and academics rather than businessmen. Does Elsevier really think that they can get away with this kind of bully-boy behaviour?
There are a couple of Elsevier journals that I've published several papers in, and I still have research that I was going to submit to them. But now I think that It's time for me to find some alternative journals to submit my work to. I'm currently reviewing one article for an Elsevier journal, and I took that task on because a friend asked me to, but after that, I won't review for any Elsevier journals. And I will not, under any circumstances, serve on the editorial board of any Elsevier journals.
Unfortunately, Elsevier is back to their old bad behaviour: they have been sending notices to researchers and academic network sites demanding the removal from the web of papers that the researchers' had published in Elsevier journals. While Elsevier may be within their legal rights to do so (since they demand that authors sign over copyright to Elsevier), preventing people from self-archiving papers that they wrote is highly
detrimental to science. In other words, Elsevier gets the research papers for free (submitted by the authors), they get the quality control for free (done by volunteer reviewers), and the administration of journals for free (done by volunteer editors). Then, they do some basic formatting and proof-reading, demand that the authors surrender all rights to the article, and publish it at an enormous profit.
Some publishers like the IEEE work the same way but allow for self-archiving, that is, they allow authors to post papers they have authored on their own websites for other researchers to access. The IEEE seems to be doing quite well out of this practice, but then the high offices of the IEEE are held by engineers and academics rather than businessmen. Does Elsevier really think that they can get away with this kind of bully-boy behaviour?
There are a couple of Elsevier journals that I've published several papers in, and I still have research that I was going to submit to them. But now I think that It's time for me to find some alternative journals to submit my work to. I'm currently reviewing one article for an Elsevier journal, and I took that task on because a friend asked me to, but after that, I won't review for any Elsevier journals. And I will not, under any circumstances, serve on the editorial board of any Elsevier journals.
Wednesday, February 5, 2014
Neural Networks new articles 2 January - 3 February, 2014
1. Safe semi-supervised learning based on weighted likelihood
Author(s): Masanori Kawakita, Jun’ichi Takeuchi
2. Effects of asymmetric and self coupling on metastable dynamical transient rotating waves in a ring of sigmoidal neurons
Author(s): Yo Horikawa
3. Kernel learning at the first level of inference
Author(s): Gavin C. Cawley, Nicola L.C. Talbot
4. Convergence behavior of delayed discrete cellular neural network without periodic coefficients
Author(s): Jinling Wang, Haijun Jiang, Cheng Hu, Tianlong Ma
5. Generalization performance of Gaussian kernels SVMC based on Markov sampling
Author(s): Jie Xu, Yuan Yan Tang, Bin Zou, Zongben Xu, Luoqing Li, Yang Lu
6. Assist-as-needed robotic trainer based on reinforcement learning and its application to dart-throwing
Author(s): Chihiro Obayashi, Tomoya Tamei, Tomohiro Shibata
7. Cross-person activity recognition using reduced kernel extreme learning machine
Author(s): Wan-Yu Deng, Qing-Hua Zheng, Zhong-Min Wang
8. Robust head pose estimation via supervised manifold learning
Author(s): Chao Wang, Xubo Song
9. Synchronization control of memristor-based recurrent neural networks with perturbations
Author(s): Weiping Wang, Lixiang Li, Haipeng Peng, Jinghua Xiao, Yixian Yang
Author(s): Masanori Kawakita, Jun’ichi Takeuchi
2. Effects of asymmetric and self coupling on metastable dynamical transient rotating waves in a ring of sigmoidal neurons
Author(s): Yo Horikawa
3. Kernel learning at the first level of inference
Author(s): Gavin C. Cawley, Nicola L.C. Talbot
4. Convergence behavior of delayed discrete cellular neural network without periodic coefficients
Author(s): Jinling Wang, Haijun Jiang, Cheng Hu, Tianlong Ma
5. Generalization performance of Gaussian kernels SVMC based on Markov sampling
Author(s): Jie Xu, Yuan Yan Tang, Bin Zou, Zongben Xu, Luoqing Li, Yang Lu
6. Assist-as-needed robotic trainer based on reinforcement learning and its application to dart-throwing
Author(s): Chihiro Obayashi, Tomoya Tamei, Tomohiro Shibata
7. Cross-person activity recognition using reduced kernel extreme learning machine
Author(s): Wan-Yu Deng, Qing-Hua Zheng, Zhong-Min Wang
8. Robust head pose estimation via supervised manifold learning
Author(s): Chao Wang, Xubo Song
9. Synchronization control of memristor-based recurrent neural networks with perturbations
Author(s): Weiping Wang, Lixiang Li, Haipeng Peng, Jinghua Xiao, Yixian Yang
Tuesday, February 4, 2014
IEEE Transactions on Evolutionary Computation, Volume 18, Number 1, February 2014
1. Guest Editorial: Special Issue on Advances in Multiobjective Evolutionary Algorithms for Data Mining
Author(s): Bandyopadhyay, S. ; Maulik, U. ; Coello, C.A.C. ; Pedrycz, W.
Pages: 1-3
2. A Survey of Multiobjective Evolutionary Algorithms for Data Mining: Part I
Author(s): A. Mukhopadhyay, U. Maulik, S. Bandyopadhyay, and C. A. Coello Coello
Pages: 4-19
3. Survey of Multiobjective Evolutionary Algorithms for Data Mining: Part II
Author(s): A. Mukhopadhyay, U. Maulik, S. Bandyopadhyay, and C. A. Coello Coello
Pages: 20-35
4. Large-Scale Experimental Evaluation of Cluster Representations for Multiobjective Evolutionary Clustering
Author(s): A. Garcia-Piquer, A. Fornells, J. Bacardit, A. Orriols-Puig, and E. Golobardes
Pages: 36-53
5. A New Multiobjective Evolutionary Algorithm for Mining a Reduced Set of Interesting Positive and Negative Quantitative Association Rules
Author(s): D. Martin, A. Rosete, J. Alcala-Fdez, and F. Herrera
Pages: 54-69
6. Population Classification in Fire Evacuation: A Multiobjective Particle Swarm Optimization Approach
Author(s): Y.-J. Zheng, H.-F. Ling, J.-Y. Xue, and S.-Y. Chen
Pages: 70-81
7. Complex Network Clustering by Multiobjective Discrete Particle Swarm Optimization Based on Decomposition
Author(s): M. Gong, Q. Cai, X. Chen, and L. Ma
Pages: 82-97
8. A Novel Graph-Based Estimation of the Distribution Algorithm and Its Extension Using Reinforcement Learning
Author(s): X. Li, S. Mabu, and K. Hirasawa
Pages: 98-113
9. Adaptive Operator Selection With Bandits for a Multiobjective Evolutionary Algorithm Based on Decomposition
Author(s): K. Li, A. Fialho, S. Kwong, and Q. Zhang
Pages: 114-131
10. Performance Metric Ensemble for Multiobjective Evolutionary Algorithms
Author(s): G. G. Yen, and Z. He
Pages: 131-144
Author(s): Bandyopadhyay, S. ; Maulik, U. ; Coello, C.A.C. ; Pedrycz, W.
Pages: 1-3
2. A Survey of Multiobjective Evolutionary Algorithms for Data Mining: Part I
Author(s): A. Mukhopadhyay, U. Maulik, S. Bandyopadhyay, and C. A. Coello Coello
Pages: 4-19
3. Survey of Multiobjective Evolutionary Algorithms for Data Mining: Part II
Author(s): A. Mukhopadhyay, U. Maulik, S. Bandyopadhyay, and C. A. Coello Coello
Pages: 20-35
4. Large-Scale Experimental Evaluation of Cluster Representations for Multiobjective Evolutionary Clustering
Author(s): A. Garcia-Piquer, A. Fornells, J. Bacardit, A. Orriols-Puig, and E. Golobardes
Pages: 36-53
5. A New Multiobjective Evolutionary Algorithm for Mining a Reduced Set of Interesting Positive and Negative Quantitative Association Rules
Author(s): D. Martin, A. Rosete, J. Alcala-Fdez, and F. Herrera
Pages: 54-69
6. Population Classification in Fire Evacuation: A Multiobjective Particle Swarm Optimization Approach
Author(s): Y.-J. Zheng, H.-F. Ling, J.-Y. Xue, and S.-Y. Chen
Pages: 70-81
7. Complex Network Clustering by Multiobjective Discrete Particle Swarm Optimization Based on Decomposition
Author(s): M. Gong, Q. Cai, X. Chen, and L. Ma
Pages: 82-97
8. A Novel Graph-Based Estimation of the Distribution Algorithm and Its Extension Using Reinforcement Learning
Author(s): X. Li, S. Mabu, and K. Hirasawa
Pages: 98-113
9. Adaptive Operator Selection With Bandits for a Multiobjective Evolutionary Algorithm Based on Decomposition
Author(s): K. Li, A. Fialho, S. Kwong, and Q. Zhang
Pages: 114-131
10. Performance Metric Ensemble for Multiobjective Evolutionary Algorithms
Author(s): G. G. Yen, and Z. He
Pages: 131-144
Monday, February 3, 2014
IEEE Transactions on Fuzzy Systems, Volume 22, Number 1 February 2014
1. Observer-Based Adaptive Decentralized Fuzzy Fault-Tolerant Control of Nonlinear Large-Scale Systems With Actuator Failures
Author(s): S. Tong, B. Huo, and Y. Li
Pages: 1-15
2. A New Possibilistic Programming Approach For Solving Fuzzy Multiobjective Assignment Problem
Author(s): P. Gupta and M. K. Mehlawat
Pages: 16-34
3. Consistency Measures for Hesitant Fuzzy Linguistic Preference Relations
Author(s): B. Zhu and Z. Xu
Pages: 35-45
4. Probabilistically Weighted OWA Aggregation
Author(s): R. R. Yager and N. Alajlan
Pages: 46-56
5. The Parameter Reduction of the Interval-Valued Fuzzy Soft Sets and Its Related Algorithms
Author(s): X. Ma, H. Qin, N. Sulaiman, T. Herawan, and J.H. Abawajy
Pages: 57-71
6. EFIS—Evolving Fuzzy Image Segmentation
Author(s): A. A. Othman, H. R. Tizhoosh, and F. Khalvati
Pages: 72-82
7. Some Hamacher Aggregation Operators Based on the Interval-Valued Intuitionistic Fuzzy Numbers and Their Application to Group Decision Making
Author(s): P. Liu
Pages: 83-97
8. Fuzzy Clustering With a Modified MRF Energy Function for Change Detection in Synthetic Aperture Radar Images
Author(s): M.Gong, L.Su, M.Jia, and W.Chen
Pages: 98-109
9. Linguistic Prototypes for Data From Eldercare Residents
Author(s): A. Wilbik, J. M. Keller, and J. C. Bezdek
Pages: 110-123
10. Stability and Stabilization of Discrete-Time T–S Fuzzy Systems With Stochastic Perturbation and Time-Varying Delay
Author(s): X. Yang, L. Wu, H.-K .Lam, and X. Su
Pages: 124-138
11. Fault Detection for T–S Fuzzy Systems With Unknown Membership Functions
Author(s): X.-J. Li and G.-H. Yang
Pages: 139-152
12. Sampled-Data Fuzzy Control of Chaotic Systems Based on a T–S Fuzzy Model
Author(s): Z.-G. Wu, P. Shi, H. Su, and J. Chu
Pages: 153-163
13. Adaptive Fuzzy Robust Output Feedback Control of Nonlinear Systems With Unknown Dead Zones Based on a Small-Gain Approach
Author(s): Y. Li, S. Tong, Y. Liu, and T. Li
Pages: 164-176
14. Amount of Information and Attitudinal-Based Method for Ranking Atanassov’s Intuitionistic Fuzzy Values
Author(s): K. Guo
Pages: 177-188
15. H-∞ Fuzzy Control With Randomly Occurring Infinite Distributed Delays and Channel Fadings
Author(s): S. Zhang, Z. Wang, D. Ding, and H. Shu
Pages: 189-200
16. An Agent-Based Fuzzy Collaborative Intelligence Approach for Precise and Accurate Semiconductor Yield Forecasting
Author(s): T. Chen and Y.-C. Wang
Pages: 201-211
17. On Energy-to-Peak Filtering for Nonuniformly Sampled Nonlinear Systems: A Markovian Jump System Approach
Author(s): H. Zhang, Y. Shi, and J. Wang
Pages: 212-222
18. Stability Analysis of Polynomial-Fuzzy-Model-Based Control Systems With Mismatched Premise Membership Functions
Author(s): H.K. Lam and S.-H. Tsai
Pages: 223-229
19. Comments on “Finite-Time H-∞ Fuzzy Control of Nonlinear Jump Systems With Time Delays Via Dynamic Observer-Based State Feedback”
Author(s): Y. Zhang, C. Liu, and H. R. Karimi
Pages: 230
Author(s): S. Tong, B. Huo, and Y. Li
Pages: 1-15
2. A New Possibilistic Programming Approach For Solving Fuzzy Multiobjective Assignment Problem
Author(s): P. Gupta and M. K. Mehlawat
Pages: 16-34
3. Consistency Measures for Hesitant Fuzzy Linguistic Preference Relations
Author(s): B. Zhu and Z. Xu
Pages: 35-45
4. Probabilistically Weighted OWA Aggregation
Author(s): R. R. Yager and N. Alajlan
Pages: 46-56
5. The Parameter Reduction of the Interval-Valued Fuzzy Soft Sets and Its Related Algorithms
Author(s): X. Ma, H. Qin, N. Sulaiman, T. Herawan, and J.H. Abawajy
Pages: 57-71
6. EFIS—Evolving Fuzzy Image Segmentation
Author(s): A. A. Othman, H. R. Tizhoosh, and F. Khalvati
Pages: 72-82
7. Some Hamacher Aggregation Operators Based on the Interval-Valued Intuitionistic Fuzzy Numbers and Their Application to Group Decision Making
Author(s): P. Liu
Pages: 83-97
8. Fuzzy Clustering With a Modified MRF Energy Function for Change Detection in Synthetic Aperture Radar Images
Author(s): M.Gong, L.Su, M.Jia, and W.Chen
Pages: 98-109
9. Linguistic Prototypes for Data From Eldercare Residents
Author(s): A. Wilbik, J. M. Keller, and J. C. Bezdek
Pages: 110-123
10. Stability and Stabilization of Discrete-Time T–S Fuzzy Systems With Stochastic Perturbation and Time-Varying Delay
Author(s): X. Yang, L. Wu, H.-K .Lam, and X. Su
Pages: 124-138
11. Fault Detection for T–S Fuzzy Systems With Unknown Membership Functions
Author(s): X.-J. Li and G.-H. Yang
Pages: 139-152
12. Sampled-Data Fuzzy Control of Chaotic Systems Based on a T–S Fuzzy Model
Author(s): Z.-G. Wu, P. Shi, H. Su, and J. Chu
Pages: 153-163
13. Adaptive Fuzzy Robust Output Feedback Control of Nonlinear Systems With Unknown Dead Zones Based on a Small-Gain Approach
Author(s): Y. Li, S. Tong, Y. Liu, and T. Li
Pages: 164-176
14. Amount of Information and Attitudinal-Based Method for Ranking Atanassov’s Intuitionistic Fuzzy Values
Author(s): K. Guo
Pages: 177-188
15. H-∞ Fuzzy Control With Randomly Occurring Infinite Distributed Delays and Channel Fadings
Author(s): S. Zhang, Z. Wang, D. Ding, and H. Shu
Pages: 189-200
16. An Agent-Based Fuzzy Collaborative Intelligence Approach for Precise and Accurate Semiconductor Yield Forecasting
Author(s): T. Chen and Y.-C. Wang
Pages: 201-211
17. On Energy-to-Peak Filtering for Nonuniformly Sampled Nonlinear Systems: A Markovian Jump System Approach
Author(s): H. Zhang, Y. Shi, and J. Wang
Pages: 212-222
18. Stability Analysis of Polynomial-Fuzzy-Model-Based Control Systems With Mismatched Premise Membership Functions
Author(s): H.K. Lam and S.-H. Tsai
Pages: 223-229
19. Comments on “Finite-Time H-∞ Fuzzy Control of Nonlinear Jump Systems With Time Delays Via Dynamic Observer-Based State Feedback”
Author(s): Y. Zhang, C. Liu, and H. R. Karimi
Pages: 230
Tuesday, January 28, 2014
Monday, January 27, 2014
Neural Networks, Volume 51, March 2014
1. Editorial Board
Pages IFC
Pages: 1-8
Author(s): Jiejie Chen, Zhigang Zeng, Ping Jiang
Pages: 9-16
Author(s):Jim Jing-Yan Wang, Halima Bensmail, Xin Gao
4. Long-term time series prediction using OP-ELM
Pages: 50-56
Author(s):Alexander Grigorievskiy, Yoan Miche, Anne-Mari Ventelä, Eric Séverin, Amaury Lendasse
5. Least Square Fast Learning Network for modeling the combustion efficiency of a 300WM coal-fired boiler
Pages: 57-66
Author(s):Guoqiang Li, Peifeng Niu, Huaibao Wang, Yongchao Liu
Pages: 17-25
Author(s):Xing He, Chuandong Li, Tingwen Huang, Chaojie Li
7. Stability analysis of switched stochastic neural networks with time-varying delays
Pages: 39-49
Author(s):Xiaotai Wu, Yang Tang, Wenbing Zhang
8. Lagrangian support vector regression via unconstrained convex minimization
Pages: 67-79
Author(s):S. Balasundaram, Deepak Gupta, Kapil
9. Periodicity and global exponential stability of generalized Cohen–Grossberg neural networks with discontinuous activations and mixed delays
Pages: 80-95
Author(s):Dongshu Wang, Lihong Huang
Pages: 26-38
Author(s):Hamid Soleimani, Arash Ahmadi, Mohammad Bavandpour, Ozra Sharifipoor
11. Current Events
Pages I-II
Pages IFC
Neuroscience
2. Global Mittag-Leffler stability and synchronization of memristor-based fractional-order neural networksPages: 1-8
Author(s): Jiejie Chen, Zhigang Zeng, Ping Jiang
Learning Systems
3. Feature selection and multi-kernel learning for sparse representation on a manifoldPages: 9-16
Author(s):Jim Jing-Yan Wang, Halima Bensmail, Xin Gao
4. Long-term time series prediction using OP-ELM
Pages: 50-56
Author(s):Alexander Grigorievskiy, Yoan Miche, Anne-Mari Ventelä, Eric Séverin, Amaury Lendasse
5. Least Square Fast Learning Network for modeling the combustion efficiency of a 300WM coal-fired boiler
Pages: 57-66
Author(s):Guoqiang Li, Peifeng Niu, Huaibao Wang, Yongchao Liu
Mathematical and Computational Analysis
6. Neural network for solving convex quadratic bilevel programming problemsPages: 17-25
Author(s):Xing He, Chuandong Li, Tingwen Huang, Chaojie Li
7. Stability analysis of switched stochastic neural networks with time-varying delays
Pages: 39-49
Author(s):Xiaotai Wu, Yang Tang, Wenbing Zhang
8. Lagrangian support vector regression via unconstrained convex minimization
Pages: 67-79
Author(s):S. Balasundaram, Deepak Gupta, Kapil
9. Periodicity and global exponential stability of generalized Cohen–Grossberg neural networks with discontinuous activations and mixed delays
Pages: 80-95
Author(s):Dongshu Wang, Lihong Huang
Engineering and Applications
10. A generalized analog implementation of piecewise linear neuron models using CCII building blocksPages: 26-38
Author(s):Hamid Soleimani, Arash Ahmadi, Mohammad Bavandpour, Ozra Sharifipoor
11. Current Events
Pages I-II
Monday, January 20, 2014
Reminder: paper submission deadline for KES 2014
A reminder that the deadline for submitting papers to the 18th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES) 2014 is 15 March, 2014. This conference will be held in Gdynia, Poland, 15-17 September, 2014.
Friday, January 17, 2014
IEEE Transactions on Neural Networks and Learning Systems, Volume 25, Issue 2, February 2014
1. What Are the Differences Between Bayesian Classifiers and Mutual-Information Classifiers?
Author(s): Bao-Gang Hu
Pages: 249 - 264
2. Multikernel Least Mean Square Algorithm
Author(s): Felipe A. Tobar; Sun-Yuan Kung; Danilo P. Mandic
Pages: 265 - 277
3. Quantum Neural Network-Based EEG Filtering for a Brain-Computer Interface
Author(s): Vaibhav Gandhi; Girijesh Prasad; Damien Coyle; Laxmidhar Behera; Thomas Martin McGinnity
Pages: 278 - 288
4. Multiclass From Binary: Expanding One-Versus-All, One-Versus-One and ECOC-Based Approaches
Author(s): Anderson Rocha; Siome Klein Goldenstein
Pages: 289 - 302
5. Short-Term Load and Wind Power Forecasting Using Neural Network-Based Prediction Intervals
Author(s): Hao Quan; Dipti Srinivasan; Abbas Khosravi
Pages: 303 - 315
6. HRLSim: A High Performance Spiking Neural Network Simulator for GPGPU Clusters
Author(s): Kirill Minkovich; Corey M. Thibeault; Michael John O’Brien; Aleksey Nogin; Youngkwan Cho; Narayan Srinivasa
Pages: 316 - 331
7. Sliding-Mode Control Design for Nonlinear Systems Using Probability Density Function Shaping
Author(s): Yu Liu; Hong Wang; Chaohuan Hou
Pages: 332 - 343
8. Nanophotonic Reservoir Computing With Photonic Crystal Cavities to Generate Periodic Patterns
Author(s): Martin Andre Agnes Fiers; Thomas Van Vaerenbergh; Francis Wyffels; David Verstraeten; Benjamin Schrauwen; Joni Dambre; Peter Bienstman
Pages: 344 - 355
9. Efficient Probabilistic Classification Vector Machine With Incremental Basis Function Selection
Author(s): Huanhuan Chen; Peter Tino; Xin Yao
Pages: 356 - 369
10. Zhang Neural Network for Online Solution of Time-Varying Linear Matrix Inequality Aided With an Equality Conversion
Author(s): Dongsheng Guo; Yunong Zhang
Pages: 370 - 382
11. Robust Pole Assignment for Synthesizing Feedback Control Systems Using Recurrent Neural Networks
Author(s): Xinyi Le; Jun Wang
Pages: 383 - 393
12. Efficient Dual Approach to Distance Metric Learning
Author(s): Chunhua Shen; Junae Kim; Fayao Liu; Lei Wang; Anton van den Hengel
Pages: 394 - 406
13. Event-Based Visual Flow
Author(s): Ryad Benosman; Charles Clercq; Xavier Lagorce; Sio-Hoi Ieng; Chiara Bartolozzi
Pages: 407 - 417
14. Decentralized Stabilization for a Class of Continuous-Time Nonlinear Interconnected Systems Using Online Learning Optimal Control Approach
Author(s): Derong Liu; Ding Wang; Hongliang Li
Pages: 418 - 428
15. Novel Adaptive Strategies for Synchronization of Linearly Coupled Neural Networks With Reaction-Diffusion Terms
Author(s): Jin-Liang Wang; Huai-Ning Wu; Lei Guo
Pages: 429 - 440
Author(s): Bao-Gang Hu
Pages: 249 - 264
2. Multikernel Least Mean Square Algorithm
Author(s): Felipe A. Tobar; Sun-Yuan Kung; Danilo P. Mandic
Pages: 265 - 277
3. Quantum Neural Network-Based EEG Filtering for a Brain-Computer Interface
Author(s): Vaibhav Gandhi; Girijesh Prasad; Damien Coyle; Laxmidhar Behera; Thomas Martin McGinnity
Pages: 278 - 288
4. Multiclass From Binary: Expanding One-Versus-All, One-Versus-One and ECOC-Based Approaches
Author(s): Anderson Rocha; Siome Klein Goldenstein
Pages: 289 - 302
5. Short-Term Load and Wind Power Forecasting Using Neural Network-Based Prediction Intervals
Author(s): Hao Quan; Dipti Srinivasan; Abbas Khosravi
Pages: 303 - 315
6. HRLSim: A High Performance Spiking Neural Network Simulator for GPGPU Clusters
Author(s): Kirill Minkovich; Corey M. Thibeault; Michael John O’Brien; Aleksey Nogin; Youngkwan Cho; Narayan Srinivasa
Pages: 316 - 331
7. Sliding-Mode Control Design for Nonlinear Systems Using Probability Density Function Shaping
Author(s): Yu Liu; Hong Wang; Chaohuan Hou
Pages: 332 - 343
8. Nanophotonic Reservoir Computing With Photonic Crystal Cavities to Generate Periodic Patterns
Author(s): Martin Andre Agnes Fiers; Thomas Van Vaerenbergh; Francis Wyffels; David Verstraeten; Benjamin Schrauwen; Joni Dambre; Peter Bienstman
Pages: 344 - 355
9. Efficient Probabilistic Classification Vector Machine With Incremental Basis Function Selection
Author(s): Huanhuan Chen; Peter Tino; Xin Yao
Pages: 356 - 369
10. Zhang Neural Network for Online Solution of Time-Varying Linear Matrix Inequality Aided With an Equality Conversion
Author(s): Dongsheng Guo; Yunong Zhang
Pages: 370 - 382
11. Robust Pole Assignment for Synthesizing Feedback Control Systems Using Recurrent Neural Networks
Author(s): Xinyi Le; Jun Wang
Pages: 383 - 393
12. Efficient Dual Approach to Distance Metric Learning
Author(s): Chunhua Shen; Junae Kim; Fayao Liu; Lei Wang; Anton van den Hengel
Pages: 394 - 406
13. Event-Based Visual Flow
Author(s): Ryad Benosman; Charles Clercq; Xavier Lagorce; Sio-Hoi Ieng; Chiara Bartolozzi
Pages: 407 - 417
14. Decentralized Stabilization for a Class of Continuous-Time Nonlinear Interconnected Systems Using Online Learning Optimal Control Approach
Author(s): Derong Liu; Ding Wang; Hongliang Li
Pages: 418 - 428
15. Novel Adaptive Strategies for Synchronization of Linearly Coupled Neural Networks With Reaction-Diffusion Terms
Author(s): Jin-Liang Wang; Huai-Ning Wu; Lei Guo
Pages: 429 - 440
Thursday, January 16, 2014
IEEE Transactions on Computational Intelligence and AI in Games, Volume 5, Number 4, December 2013
1. A Survey of Real-Time Strategy Game AI Research and Competition in StarCraft
Author(s): Ontanon, S. ; Synnaeve, G. ; Uriarte, A. ; Richoux, F. ; Churchill, D. ; Preuss, M.
Pages: 293-311
2. Repeated Goofspiel: A Game of Pure Strategy
Author(s):Dror, M. ; Kendall, G.
Pages: 312-324
3. A Heuristic-Based Planner and Improved Controller for a Two-Layered Approach for the Game of Billiards
Author(s):Landry, J.-F. ; Dussault, J.-P. ; Mahey, P.
Pages: 325-336
4. Automated 3-D Animation From Snooker Videos With Information-Theoretical Optimization
Author(s):Jiang, R. ; Parry, M.L. ; Legg, P.A. ; Chung, D.H.S. ; Griffiths, I.W.
Pages: 337-345
5. Incentive Learning in Monte Carlo Tree Search
Author(s):Kao, K.-Y. ; Wu, I-C. ; Yen, S.-J. ; Shan, Y.-C.
Pages: 346-352
Author(s): Ontanon, S. ; Synnaeve, G. ; Uriarte, A. ; Richoux, F. ; Churchill, D. ; Preuss, M.
Pages: 293-311
2. Repeated Goofspiel: A Game of Pure Strategy
Author(s):Dror, M. ; Kendall, G.
Pages: 312-324
3. A Heuristic-Based Planner and Improved Controller for a Two-Layered Approach for the Game of Billiards
Author(s):Landry, J.-F. ; Dussault, J.-P. ; Mahey, P.
Pages: 325-336
4. Automated 3-D Animation From Snooker Videos With Information-Theoretical Optimization
Author(s):Jiang, R. ; Parry, M.L. ; Legg, P.A. ; Chung, D.H.S. ; Griffiths, I.W.
Pages: 337-345
5. Incentive Learning in Monte Carlo Tree Search
Author(s):Kao, K.-Y. ; Wu, I-C. ; Yen, S.-J. ; Shan, Y.-C.
Pages: 346-352
Wednesday, January 15, 2014
IEEE Transactions on Autonomous Mental Development, Volume 5, Number 4, December 2013
1. Computational Audiovisual Scene Analysis in Online Adaptation of Audio-Motor Maps
Author(s): Yan, R. ; Rodemann, T. ; Wrede, B.
Pages: 273-287
2. Modeling Cross-Modal Interactions in Early Word Learning
Author(s): Althaus, N. ; Mareschal, D.
Pages: 288-297
3. Learning to Reproduce Fluctuating Time Series by Inferring Their Time-Dependent Stochastic Properties: Application in Robot Learning Via Tutoring
Author(s): Murata, S. ; Namikawa, J. ; Arie, H. ; Sugano, S. ; Tani, J.
Pages: 298-310
4. Conceptual Imitation Learning Based on Perceptual and Functional Characteristics of Action
Author(s): Hajimirsadeghi, H. ; Ahmadabadi, M.N. ; Araabi, B.N.
Pages: 311-325
5. A Robotic Model of Reaching and Grasping Development
Author(s): Savastano, P. ; Nolfi, S.
Pages: 326-337
Author(s): Yan, R. ; Rodemann, T. ; Wrede, B.
Pages: 273-287
2. Modeling Cross-Modal Interactions in Early Word Learning
Author(s): Althaus, N. ; Mareschal, D.
Pages: 288-297
3. Learning to Reproduce Fluctuating Time Series by Inferring Their Time-Dependent Stochastic Properties: Application in Robot Learning Via Tutoring
Author(s): Murata, S. ; Namikawa, J. ; Arie, H. ; Sugano, S. ; Tani, J.
Pages: 298-310
4. Conceptual Imitation Learning Based on Perceptual and Functional Characteristics of Action
Author(s): Hajimirsadeghi, H. ; Ahmadabadi, M.N. ; Araabi, B.N.
Pages: 311-325
5. A Robotic Model of Reaching and Grasping Development
Author(s): Savastano, P. ; Nolfi, S.
Pages: 326-337
Tuesday, January 14, 2014
Neural Networks new articles 6 January -12 January, 2014
1. Nonsmooth finite-time stabilization of neural networks with discontinuous activations
Xiaoyang Liu, Ju.H. Park, Nan Jiang, Jinde Cao
Xiaoyang Liu, Ju.H. Park, Nan Jiang, Jinde Cao
Thursday, January 2, 2014
Neural Networks new articles 23 December - 29 December, 2013
1. Construction of a Boolean model of gene and protein regulatory network with memory
Meng Yang, Rui Li, Tianguang Chu
2. Pairwise constrained concept factorization for data representation
Yangcheng He, Hongtao Lu, Lei Huang, Saining Xie
3. Periodicity and global exponential stability of a generalized Cohen–Grossberg neural networks with discontinuous activations and mixed delays
Dongshu Wang, Lihong Huang
Meng Yang, Rui Li, Tianguang Chu
2. Pairwise constrained concept factorization for data representation
Yangcheng He, Hongtao Lu, Lei Huang, Saining Xie
3. Periodicity and global exponential stability of a generalized Cohen–Grossberg neural networks with discontinuous activations and mixed delays
Dongshu Wang, Lihong Huang
Wednesday, January 1, 2014
IEEE Transactions on Neural Networks and Learning Systems: Volume 25, Issue 1, January 2014
1. Guest Editorial: Learning in Nonstationary and Evolving Environments
Author(s): Robi Polikar; Cesare Alippi
Pages: 9 - 11
2. COMPOSE: A Semisupervised Learning Framework for Initially Labeled Nonstationary Streaming Data
Author(s): Karl B. Dyer; Robert Capo; Robi Polikar
Pages: 12 - 26
3. Active Learning With Drifting Streaming Data
Author(s): Indre Zliobaite; Albert Bifet; Bernhard Pfahringer; Geoffrey Holmes
Pages: 27 - 39
4. Online Bayesian Learning With Natural Sequential Prior Distribution
Author(s): Yohei Nakada; Makio Wakahara; Takashi Matsumoto
Pages: 40 - 54
5. PANFIS: A Novel Incremental Learning Machine
Author(s): Mahardhika Pratama; Sreenatha. G. Anavatti; Plamen. P. Angelov; Edwin Lughofer
Pages: 55 - 68
6. PCA Feature Extraction for Change Detection in Multidimensional Unlabeled Data
Author(s): Ludmila I. Kuncheva; William J. Faithfull
Pages: 69 - 80
7. Reacting to Different Types of Concept Drift: The Accuracy Updated Ensemble Algorithm
Author(s): Dariusz Brzezinski; Jerzy Stefanowski
Pages: 81 - 94
8. Mining Recurring Concepts in a Dynamic Feature Space
Author(s): Joao Bartolo Gomes; Mohamed Medhat Gaber; Pedro A. C. Sousa; Ernestina Menasalvas
Pages: 95 - 110
9. Dynamic Learning From Adaptive Neural Network Control of a Class of Nonaffine Nonlinear Systems
Author(s): Shi-Lu Dai; Cong Wang; Min Wang
Pages: 111 - 123
10. Learning in the Model Space for Cognitive Fault Diagnosis
Author(s): Huanhuan Chen; Peter Tino; Ali Rodan; Xin Yao
Pages: 124 - 136
11. Adaptive Approximation for Multiple Sensor Fault Detection and Isolation of Nonlinear Uncertain Systems
Author(s): Vasso Reppa; Marios M. Polycarpou; Christos G. Panayiotou
Pages: 137 - 153
12. Dealing With Concept Drifts in Process Mining
Author(s): R. P. Jagadeesh Chandra Bose; Wil M. P. van der Aalst; Indre Zliobaite; Mykola Pechenizkiy
Pages: 154 - 171
13. Adaptive Convex Combination Approach for the Identification of Improper Quaternion Processes
Author(s): Bukhari Che Ujang; Cyrus Jahanchahi; Clive Cheong Took; Danilo P. Mandic
Pages: 172 - 182
14. Developmental Perception of the Self and Action
Author(s): Ryo Saegusa; Giorgio Metta; Giulio Sandini; Lorenzo Natale
Pages: 183 - 202
15. Linguistic Decision Making for Robot Route Learning
Author(s): Hongmei He; Thomas Martin McGinnity; Sonya Coleman; Bryan Gardiner
Pages: 203 - 215
16. An Interval Type-2 Neural Fuzzy Chip With On-Chip Incremental Learning Ability for Time-Varying Data Sequence Prediction and System Control
Author(s): Chia-Feng Juang; Chi-You Chen
Pages: 216 - 228
17. Learning Geotemporal Nonstationary Failure and Recovery of Power Distribution
Author(s): Yun Wei; Chuanyi Ji; Floyd Galvan; Stephen Couvillon; George Orellana; James Momoh
Pages: 229 - 240
18. Continuous Dynamical Combination of Short and Long-Term Forecasts for Nonstationary Time Series
Author(s): Domingos Savio Pereira Salazar; Paulo Jorge Leitao Adeodato; Adrian Lucena Arnaud
Pages: 241 - 246
Author(s): Robi Polikar; Cesare Alippi
Pages: 9 - 11
2. COMPOSE: A Semisupervised Learning Framework for Initially Labeled Nonstationary Streaming Data
Author(s): Karl B. Dyer; Robert Capo; Robi Polikar
Pages: 12 - 26
3. Active Learning With Drifting Streaming Data
Author(s): Indre Zliobaite; Albert Bifet; Bernhard Pfahringer; Geoffrey Holmes
Pages: 27 - 39
4. Online Bayesian Learning With Natural Sequential Prior Distribution
Author(s): Yohei Nakada; Makio Wakahara; Takashi Matsumoto
Pages: 40 - 54
5. PANFIS: A Novel Incremental Learning Machine
Author(s): Mahardhika Pratama; Sreenatha. G. Anavatti; Plamen. P. Angelov; Edwin Lughofer
Pages: 55 - 68
6. PCA Feature Extraction for Change Detection in Multidimensional Unlabeled Data
Author(s): Ludmila I. Kuncheva; William J. Faithfull
Pages: 69 - 80
7. Reacting to Different Types of Concept Drift: The Accuracy Updated Ensemble Algorithm
Author(s): Dariusz Brzezinski; Jerzy Stefanowski
Pages: 81 - 94
8. Mining Recurring Concepts in a Dynamic Feature Space
Author(s): Joao Bartolo Gomes; Mohamed Medhat Gaber; Pedro A. C. Sousa; Ernestina Menasalvas
Pages: 95 - 110
9. Dynamic Learning From Adaptive Neural Network Control of a Class of Nonaffine Nonlinear Systems
Author(s): Shi-Lu Dai; Cong Wang; Min Wang
Pages: 111 - 123
10. Learning in the Model Space for Cognitive Fault Diagnosis
Author(s): Huanhuan Chen; Peter Tino; Ali Rodan; Xin Yao
Pages: 124 - 136
11. Adaptive Approximation for Multiple Sensor Fault Detection and Isolation of Nonlinear Uncertain Systems
Author(s): Vasso Reppa; Marios M. Polycarpou; Christos G. Panayiotou
Pages: 137 - 153
12. Dealing With Concept Drifts in Process Mining
Author(s): R. P. Jagadeesh Chandra Bose; Wil M. P. van der Aalst; Indre Zliobaite; Mykola Pechenizkiy
Pages: 154 - 171
13. Adaptive Convex Combination Approach for the Identification of Improper Quaternion Processes
Author(s): Bukhari Che Ujang; Cyrus Jahanchahi; Clive Cheong Took; Danilo P. Mandic
Pages: 172 - 182
14. Developmental Perception of the Self and Action
Author(s): Ryo Saegusa; Giorgio Metta; Giulio Sandini; Lorenzo Natale
Pages: 183 - 202
15. Linguistic Decision Making for Robot Route Learning
Author(s): Hongmei He; Thomas Martin McGinnity; Sonya Coleman; Bryan Gardiner
Pages: 203 - 215
16. An Interval Type-2 Neural Fuzzy Chip With On-Chip Incremental Learning Ability for Time-Varying Data Sequence Prediction and System Control
Author(s): Chia-Feng Juang; Chi-You Chen
Pages: 216 - 228
17. Learning Geotemporal Nonstationary Failure and Recovery of Power Distribution
Author(s): Yun Wei; Chuanyi Ji; Floyd Galvan; Stephen Couvillon; George Orellana; James Momoh
Pages: 229 - 240
18. Continuous Dynamical Combination of Short and Long-Term Forecasts for Nonstationary Time Series
Author(s): Domingos Savio Pereira Salazar; Paulo Jorge Leitao Adeodato; Adrian Lucena Arnaud
Pages: 241 - 246
Monday, December 23, 2013
Neural Networks Volume 50 Pages 1-182 February 2014
Neural Networks Letters
1. Existence and global exponential stability of periodic solution for high-order discrete-time BAM neural networksPages: 98-109
Author(s): Ancai Zhang, Jianlong Qiu, Jinhua She
2. Cellular computational networks—A scalable architecture for learning the dynamics of large networked systems
Pages: 120-123
Author(s): Bipul Luitel, Ganesh Kumar Venayagamoorthy
Cognitive Science
3. Supervised orthogonal discriminant subspace projects learning for face recognitionPages: 33-46
Author(s): Yu Chen, Xiao-Hong Xu
Learning Systems
4. Direct Kernel Perceptron (DKP): Ultra-fast kernel ELM-based classification with non-iterative closed-form weight calculationPages: 60-71
Author(s): Manuel Fernández-Delgado, Eva Cernadas, Senén Barro, Jorge Ribeiro, José Neves
5. Batch gradient method with smoothing image regularization for training of feedforward neural networks
Pages: 72-78
Author(s): Wei Wu, Qinwei Fan, Jacek M. Zurada, Jian Wang, Dakun Yang, Yan Liu
6. Compressed classification learning with Markov chain samples
Pages: 90-97
Author(s): Feilong Cao, Tenghui Dai, Yongquan Zhang, Yuanpeng Tan
7. Semi-supervised learning of class balance under class-prior change by distribution matching
Pages: 110-119
Author(s): Marthinus Christoffel du Plessis, Masashi Sugiyama
8. Robust support vector machine-trained fuzzy system
Pages: 154-165
Author(s): Yahya Forghani, Hadi Sadoghi Yazdi
9. Large-scale linear nonparallel support vector machine solver
Pages: 166-174
Author(s): Yingjie Tian, Yuan Ping
10. Finite time convergent learning law for continuous neural networks
Pages: 175-182
Author(s): Isaac Chairez
Mathematical and Computational Analysis
11. A Bayesian inverse solution using independent component analysisPages: 47-59
Author(s): Jouni Puuronen, Aapo Hyvärinen
12. A one-layer recurrent neural network for constrained nonsmooth invex optimization
Pages: 79-89
Author(s): Guocheng Li, Zheng Yan, Jun Wang
13. Pointwise probability reinforcements for robust statistical inference
Pages: 124-141
Author(s): Benoît Frénay, Michel Verleysen
14. A linear recurrent kernel online learning algorithm with sparse updates
Pages: 142-153
Author(s): Haijin Fan, Qing Song
Engineering and Applications
15. Correcting and combining time series forecastersPages: 1-11
Author(s): Paulo Renato A. Firmino, Paulo S.G. de Mattos Neto, Tiago A.E. Ferreira
16. Hybrid fault diagnosis of nonlinear systems using neural parameter estimators
Pages: 12-32
Author(s): E. Sobhani-Tehrani, H.A. Talebi, K. Khorasani
Thursday, December 19, 2013
Work-Life Balance
A video of a panel session of two young(ish) professors, talking about work-life balance. I used to work in the same lab as Corey Bradshaw, and have even published with him. He is a straight-speaking person who says what he thinks, so you can be sure that what he says in this session is his honest opinion. That being said, I do have some conflicted feelings about this discussion.
On the one hand, I respect and envy their academic achievements. On the other hand, the sacrifices they have made to get where they are are just terrifying, and strike me as selfish. When Tanya Munro talks about dragging her two tiny babies to a conference, or skipping their end-of-year performance, or both of them talking about leaving their kids at home for the evening again, I can't help but think that that is just so much macho bullsh*t. For me, family comes first, there is no choice. My daughter is smart without being conceited, brave without being reckless, strong without being over-bearing, loving, caring and empathetic, without being clingy. She wouldn't be those things if she didn't have an extended family around her who were fully engaged in her upbringing. I'd rather be a "less-successful" academic, than risk losing what I have with her. She'll grow up soon enough, I can put more energy into my career then. Certainly I could achieve more if I sacrificed more, or if I slept a lot less, but if I worked an 80-hour week, I would die. It's as simple as that. One of the last things my father said to me, just a few days before he died, was "you're not a machine". I refuse to risk depriving my wife of her husband and my daughter of her father.
Another point at which I disagree with Corey is when he mentions telling his post-docs "Start publishing papers or I'm going to have to sack you". I think this is a poor management technique: if someone isn't performing, you as a manager must coach them to lift their game. Management by fear is a poor technique and just breeds resentment. Life may be too short to work with a*holes, but it's also too short to accumulate enemies.
The idea that everything you do should lead to an obvious paper is fine for someone who in only doing research, but personally I couldn't live without undergraduate teaching. I need the surge of energy I get from standing in front of a class and explaining complex concepts. I really love it when someone "gets it", when their face lights up with understanding. Even though I spend most of my time now in management, I'd never take another position where I wasn't teaching.
Finally, everyone's circumstances are different, so you shouldn't compare yourself to others. Tanya Munro may have met her husband during her first year of university, but I met my wife at 28, married her just before turning 30, and had my daughter, finished my PhD, and started my first post-doc at 31. I had problems with my PhD topic, I had health problems, and I had to work to support myself. Now I'm 40 and my career path is finally starting to settle down. I may have achieved less academically than them, but so have most people. What, really, does comparing myself to them achieve? Nothing, except perhaps to make me feel badly about myself.
One thing I do agree with is that you have to find your own balance, your own way. I think I've found mine, and I'm happier for it. The video is well worth the time to view it, if only to gain some perspectives from successful academics.
On the one hand, I respect and envy their academic achievements. On the other hand, the sacrifices they have made to get where they are are just terrifying, and strike me as selfish. When Tanya Munro talks about dragging her two tiny babies to a conference, or skipping their end-of-year performance, or both of them talking about leaving their kids at home for the evening again, I can't help but think that that is just so much macho bullsh*t. For me, family comes first, there is no choice. My daughter is smart without being conceited, brave without being reckless, strong without being over-bearing, loving, caring and empathetic, without being clingy. She wouldn't be those things if she didn't have an extended family around her who were fully engaged in her upbringing. I'd rather be a "less-successful" academic, than risk losing what I have with her. She'll grow up soon enough, I can put more energy into my career then. Certainly I could achieve more if I sacrificed more, or if I slept a lot less, but if I worked an 80-hour week, I would die. It's as simple as that. One of the last things my father said to me, just a few days before he died, was "you're not a machine". I refuse to risk depriving my wife of her husband and my daughter of her father.
Another point at which I disagree with Corey is when he mentions telling his post-docs "Start publishing papers or I'm going to have to sack you". I think this is a poor management technique: if someone isn't performing, you as a manager must coach them to lift their game. Management by fear is a poor technique and just breeds resentment. Life may be too short to work with a*holes, but it's also too short to accumulate enemies.
The idea that everything you do should lead to an obvious paper is fine for someone who in only doing research, but personally I couldn't live without undergraduate teaching. I need the surge of energy I get from standing in front of a class and explaining complex concepts. I really love it when someone "gets it", when their face lights up with understanding. Even though I spend most of my time now in management, I'd never take another position where I wasn't teaching.
Finally, everyone's circumstances are different, so you shouldn't compare yourself to others. Tanya Munro may have met her husband during her first year of university, but I met my wife at 28, married her just before turning 30, and had my daughter, finished my PhD, and started my first post-doc at 31. I had problems with my PhD topic, I had health problems, and I had to work to support myself. Now I'm 40 and my career path is finally starting to settle down. I may have achieved less academically than them, but so have most people. What, really, does comparing myself to them achieve? Nothing, except perhaps to make me feel badly about myself.
One thing I do agree with is that you have to find your own balance, your own way. I think I've found mine, and I'm happier for it. The video is well worth the time to view it, if only to gain some perspectives from successful academics.
Wednesday, December 18, 2013
WCCI 2014 Deadline Extension
The deadline for submitting papers to the World Congress on Computational Intelligence (WCCI) 2014 has been extended to the 20th of January, 2014. There will be no further extensions. This conference combines the three major conferences of the IEEE Computational Intelligence Society: The International Joint Conference on Neural Networks (IJCNN); the Congress on Evolutionary Computation (CEC) and the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). This conference will be held in Beijing, China, July 6-11, 2014.
Monday, December 16, 2013
Reminder: paper submission deadline for EAIS 2014
A reminder that the deadline for submitting papers to the IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) 2014 is January 15, 2014. This conference will be held in Linz, Austria, June 2-4, 2014.
Friday, December 13, 2013
Reminder: paper submission deadline for IEEE SSCI 2014
A reminder that the deadline for submitting papers to the IEEE Symposium Series on Computational Intelligence (SSCI) 2014 is 15 June 2014. This group of symposia will be held in Orlando, Florida, 9-12 December, 2014.
Tuesday, December 10, 2013
Call for papers: Special Session for WCCI 2014 "Applications of Computational lntelligence in Ecological Informatics and Environmental Modelling"
Aim
The aim of this special session is to provide a forum for recent research in the application of computational intelligence in the areas of ecological informatics, ecological modelling and environmental modelling.Ecological informatics and the related field of ecological modelling involve constructing computational models of ecological systems. These models include such things as the distribution or abundance of particular species, models of the interaction between multiple species, and models of the future development of populations. Environmental modelling is closely related and involves constructing models of the physical environment that biological eco-systems inhabit. These models cover such topics as the climate and climate change and the detection of landscape features from geographical data. Models have also been constructed of such environmental topics as waste management systems, water quality and drainage systems and air pollution. As these are highly-complex systems, algorithms from the field of computational intelligence have already been widely applied to modelling this data. Previous work has successfully applied artificial neural networks, fuzzy systems, evolutionary algorithms, support vector machines and combinations of these including neuro-fuzzy and neuro-evolutionary approaches. In each case, computational intelligence methods were shown to be more effective at solving the problem than the alternative methods.
Scope
Topics relevant to this special session include, but are not limited to, the following applications of computational intelligence, including Artificial Neural Networks, Fuzzy Systems, and Evolutionary Algorithms:• Species distribution and ecological niche modelling
• Predicting species abundance
• Remote sensing image analysis and content-based image retrieval for Ecological Informatics and Environmental Modelling
• Analysis of species assemblages
• Issues in the preparation of ecological data for modelling
• Modelling of pollutants in air, land or water
• Modelling water quality
• Predicting the effects of climate change
• Predicting crop hazards, pests or diseases
• Identifying landscape features
• Modelling ecosystem biomass
Deadline
The deadline for submissions to this special session is 20 January 2014.Information for Authors
1) Information on the format and templates for papers can be found here:http://www.ieee-wcci2014.org/Paper%20Submission.htm
2) Papers should be submitted via the IJCNN 2014 paper submission site:
http://ieee-cis.org/conferences/ijcnn2014/upload.php3)
Select the Special Session name in the Main Research topic dropdown list
4) Fill out the input fields, upload the PDF file of your paper and finalize your submission by the deadline of December 20, 2013
Organisers
• Dr Michael J Watts, AIS St Helens, Auckland, New Zealand. mjwatts@ieee.org• Associate Professor Russel Pears, Auckland University of Technology, Auckland, New Zealand, russel.pears@aut.ac.nz
• Professor Jie Yang, Shanghai Jiao Tong University, Shanghai, China, jieyang@sjtu.edu.cn
Friday, December 6, 2013
IEEE Transactions on Fuzzy Systems, Volume 21, Number 6, December 2013
1. An Improved Estimation Method for Unmodeled Dynamics Based on ANFIS and Its Application to Controller Design
Author(s): Zhang, Y. ; Chai, T. ; Wang, H. ; Chen, X. ; Su, C.-Y.
Pages: 989-1005
2. Linguistic Computational Model Based on 2-Tuples and Intervals
Author(s): Dong, Y. ; Zhang, G. ; Hong, W.-C. ; Yu, S.
Pages: 1006-1018
3. Dynamic Fuzzy Clustering and Its Application in Motion Segmentation
Author(s): Nguyen, T.M. ; Wu, Q.M.J.
Pages: 1019-1031
4. Hierarchical Structured Sparse Representation for T–S Fuzzy Systems Identification
Author(s): Luo, M. ; Sun, F. ; Liu, H.
Pages: 1032-1043
5. Intelligent Hybrid Control System Design for Antilock Braking Systems Using Self-Organizing Function-Link Fuzzy Cerebellar Model Articulation Controller
Author(s): Lin, C.-M. ; Li, H.-Y.
Pages: 1044-1055
6. Simplified Interval Type-2 Fuzzy Logic Systems
Author(s): Mendel, J.M. ; Liu, X.
Pages: 1056-1069
7. Statistical Inference of Rough Set Dependence and Importance Analysis
Author(s): Hu, D. ; Yu, X.
Pages: 1070-1079
8. A Metacognitive Neuro-Fuzzy Inference System (McFIS) for Sequential Classification Problems
Author(s): Subramanian, K. ; Suresh, S. ; Sundararajan, N.
Pages: 1080-1095
9. Strongest Strong Cycles and $theta$-Fuzzy Graphs
Author(s): Mathew, S. ; Sunitha, M.S.
Pages: 1096-1104
10. Functional Machine With Takagi–Sugeno Inference to Coordinated Movement in Underwater Vehicle-Manipulator Systems
Author(s): dos Santos, C.H.F. ; De Pieri, E.R.
Pages: 1105-1114
11. Human Reliability Evaluation for Offshore Platform Musters Using Intuitionistic Fuzzy Sets
Author(s): Tyagi, S.K. ; Akram, M.
Pages: 1115-1122
12. Enhanced Adaptive Fuzzy Control With Optimal Approximation Error Convergence
Author(s): Pan, Y. ; Er, M.J.
Pages: 1123-1132
13. FINGRAMS: Visual Representations of Fuzzy Rule-Based Inference for Expert Analysis of Comprehensibility
Author(s): Pancho, D.P. ; Alonso, J.M. ; Cordon, O. ; Quirin, A. ; Magdalena, L.
Pages: 1133-1149
14. A New Approach to Interval-Valued Choquet Integrals and the Problem of Ordering in Interval-Valued Fuzzy Set Applications
Author(s): Bustince, H. ; Galar, M. ; Bedregal, B. ; Kolesarova, A. ; Mesiar, R.
Pages: 1150-1162
15. Defuzzification Functionals of Ordered Fuzzy Numbers
Author(s): Kosinski, W. ; Prokopowicz, P. ; Rosa, A.
Pages: 1163-1169
16. A Soft Modularity Function For Detecting Fuzzy Communities in Social Networks
Author(s): Havens, T.C. ; Bezdek, J.C. ; Leckie, C. ; Ramamohanarao, K. ; Palaniswami, M.
Pages: 1170-1175
Author(s): Zhang, Y. ; Chai, T. ; Wang, H. ; Chen, X. ; Su, C.-Y.
Pages: 989-1005
2. Linguistic Computational Model Based on 2-Tuples and Intervals
Author(s): Dong, Y. ; Zhang, G. ; Hong, W.-C. ; Yu, S.
Pages: 1006-1018
3. Dynamic Fuzzy Clustering and Its Application in Motion Segmentation
Author(s): Nguyen, T.M. ; Wu, Q.M.J.
Pages: 1019-1031
4. Hierarchical Structured Sparse Representation for T–S Fuzzy Systems Identification
Author(s): Luo, M. ; Sun, F. ; Liu, H.
Pages: 1032-1043
5. Intelligent Hybrid Control System Design for Antilock Braking Systems Using Self-Organizing Function-Link Fuzzy Cerebellar Model Articulation Controller
Author(s): Lin, C.-M. ; Li, H.-Y.
Pages: 1044-1055
6. Simplified Interval Type-2 Fuzzy Logic Systems
Author(s): Mendel, J.M. ; Liu, X.
Pages: 1056-1069
7. Statistical Inference of Rough Set Dependence and Importance Analysis
Author(s): Hu, D. ; Yu, X.
Pages: 1070-1079
8. A Metacognitive Neuro-Fuzzy Inference System (McFIS) for Sequential Classification Problems
Author(s): Subramanian, K. ; Suresh, S. ; Sundararajan, N.
Pages: 1080-1095
9. Strongest Strong Cycles and $theta$-Fuzzy Graphs
Author(s): Mathew, S. ; Sunitha, M.S.
Pages: 1096-1104
10. Functional Machine With Takagi–Sugeno Inference to Coordinated Movement in Underwater Vehicle-Manipulator Systems
Author(s): dos Santos, C.H.F. ; De Pieri, E.R.
Pages: 1105-1114
11. Human Reliability Evaluation for Offshore Platform Musters Using Intuitionistic Fuzzy Sets
Author(s): Tyagi, S.K. ; Akram, M.
Pages: 1115-1122
12. Enhanced Adaptive Fuzzy Control With Optimal Approximation Error Convergence
Author(s): Pan, Y. ; Er, M.J.
Pages: 1123-1132
13. FINGRAMS: Visual Representations of Fuzzy Rule-Based Inference for Expert Analysis of Comprehensibility
Author(s): Pancho, D.P. ; Alonso, J.M. ; Cordon, O. ; Quirin, A. ; Magdalena, L.
Pages: 1133-1149
14. A New Approach to Interval-Valued Choquet Integrals and the Problem of Ordering in Interval-Valued Fuzzy Set Applications
Author(s): Bustince, H. ; Galar, M. ; Bedregal, B. ; Kolesarova, A. ; Mesiar, R.
Pages: 1150-1162
15. Defuzzification Functionals of Ordered Fuzzy Numbers
Author(s): Kosinski, W. ; Prokopowicz, P. ; Rosa, A.
Pages: 1163-1169
16. A Soft Modularity Function For Detecting Fuzzy Communities in Social Networks
Author(s): Havens, T.C. ; Bezdek, J.C. ; Leckie, C. ; Ramamohanarao, K. ; Palaniswami, M.
Pages: 1170-1175
Thursday, December 5, 2013
IEEE Transactions on Evolutionary Computation, Volume 17, Number 6, December 2013
1. Striking a Mean- and Parent-Centric Balance in Real-Valued Crossover Operators
Author(s): Someya, H.
Pages: 737-754
2. Multiobjective Particle Swarm Optimization With Preference-Based Sort and Its Application to Path Following Footstep Optimization for Humanoid Robots
Author(s): Lee, K.-B. ; Kim, J.-H.
Pages: 755-766
3. An Energy-Based Sampling Technique for Multi-Objective Restricted Boltzmann Machine
Author(s): Shim, V.A. ; Tan, K.C. ; Cheong, C.Y.
Pages: 767-785
4. An Efficient Evolutionary Algorithm for Chance-Constrained Bi-Objective Stochastic Optimization
Author(s): Liu, B. ; Zhang, Q. ; Fernandez, F.V. ; Gielen, G.G.E.
Pages: 786-796
5. Scaling Up Estimation of Distribution Algorithms for Continuous Optimization
Author(s): Dong, W. ; Chen, T. ; Tino, P. ; Yao, X.
Pages: 797-822
6. Optimal Cycle Program of Traffic Lights With Particle Swarm Optimization
Author(s): Garcia-Nieto, J. ; Olivera, A.C. ; Alba, E.
Pages: 823-839
7. Grammatical Evolution Hyper-Heuristic for Combinatorial Optimization Problems
Author(s): Sabar, N.R. ; Ayob, M. ; Kendall, G. ; Qu, R.
Pages: 840-861
8. Fitness Modeling With Markov Networks
Author(s): Brownlee, A.E.I. ; McCall, J.A.W. ; Zhang, Q.
Pages: 862-879
Author(s): Someya, H.
Pages: 737-754
2. Multiobjective Particle Swarm Optimization With Preference-Based Sort and Its Application to Path Following Footstep Optimization for Humanoid Robots
Author(s): Lee, K.-B. ; Kim, J.-H.
Pages: 755-766
3. An Energy-Based Sampling Technique for Multi-Objective Restricted Boltzmann Machine
Author(s): Shim, V.A. ; Tan, K.C. ; Cheong, C.Y.
Pages: 767-785
4. An Efficient Evolutionary Algorithm for Chance-Constrained Bi-Objective Stochastic Optimization
Author(s): Liu, B. ; Zhang, Q. ; Fernandez, F.V. ; Gielen, G.G.E.
Pages: 786-796
5. Scaling Up Estimation of Distribution Algorithms for Continuous Optimization
Author(s): Dong, W. ; Chen, T. ; Tino, P. ; Yao, X.
Pages: 797-822
6. Optimal Cycle Program of Traffic Lights With Particle Swarm Optimization
Author(s): Garcia-Nieto, J. ; Olivera, A.C. ; Alba, E.
Pages: 823-839
7. Grammatical Evolution Hyper-Heuristic for Combinatorial Optimization Problems
Author(s): Sabar, N.R. ; Ayob, M. ; Kendall, G. ; Qu, R.
Pages: 840-861
8. Fitness Modeling With Markov Networks
Author(s): Brownlee, A.E.I. ; McCall, J.A.W. ; Zhang, Q.
Pages: 862-879
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