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

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.

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

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.

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 Angelov
University of Lancaster, UK

Important Dates

April 7, 2014
Submission of a full-length paper

May 25, 2014
Acceptance/Rejection notification

July 9, 2014
Final camera-ready paper submission

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.

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

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

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

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

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

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.

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

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.

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

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

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

Neural Networks new articles 20 January - 26 January, 2014

1. Policy oscillation is overshooting
Paul Wagner
   
2. NeuCube: A spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data
Nikola K. Kasabov

Neural Networks, Volume 51, March 2014

1. Editorial Board  
Pages IFC

Neuroscience

2. Global Mittag-Leffler stability and synchronization of memristor-based fractional-order neural networks  
Pages: 1-8
Author(s): Jiejie Chen, Zhigang Zeng, Ping Jiang
   

Learning Systems

3. Feature selection and multi-kernel learning for sparse representation on a manifold  
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
   

Mathematical and Computational Analysis

6. Neural network for solving convex quadratic bilevel programming problems 
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
   

Engineering and Applications

10. A generalized analog implementation of piecewise linear neuron models using CCII building blocks  
Pages: 26-38
Author(s):Hamid Soleimani, Arash Ahmadi, Mohammad Bavandpour, Ozra Sharifipoor
   
11. Current Events  
Pages I-II

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.

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

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

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

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