IEEE Transactions on Computational Intelligence and Artificial Intelligence in Games, Volume 6, Issue 3, September 2014

1. Real-Time Monte Carlo Tree Search in Ms Pac-Man
Author(s): T. Pepels, M. H. M. Winands, and M. Lanctot
Pages: 245-257

2. An Automatically Generated Evaluation Function in General Game Playing
Author(s): K. Walędzik and J. Mańdziuk
Pages: 258-270

3. A Computational Model of Plan-Based Narrative Conflict at the Fabula Level
Author(s): S. G. Ware, R. M. Young, B. Harrison, and D. L. Roberts
Pages: 271-288

4. Good Machine Performance in Turing’s Imitation Game
Author(s): K. Warwick and H. Shah
Pages: 289-300

5. Preference Learning for Move Prediction and Evaluation Function Approximation in Othello
Author(s): T. P. Runarsson and S. M. Lucas
Pages: 300-313

IEEE Transactions on Neural Networks and Learning Systems: Volume 25, Number 9, September 2014

SPECIAL ISSUE ON COMPLEX- AND HYPERCOMPLEX-VALUED NEURAL NETWORKS

1. Guest Editorial: Special Issue on Complex- and Hypercomplex-Valued Neural Networks
Author(s): A. Hirose, I. Aizenberg, and D. P. Mandic
Page(s): 1597-1599

SPECIAL ISSUE PAPERS

2. Complex-Valued Recurrent Correlation Neural Networks
Author(s): M. E. Valle
Page(s): 1600-1612

3. The Field of Values of a Matrix and Neural Networks
Author(s): G. M. Georgiou
Page(s): 1613-1620

4. Different Complex ZFs Leading to Different Complex ZNN Models for Time-Varying Complex Generalized Inverse Matrices
Author(s): B. Liao and Y. Zhang
Page(s): 1621-1631

5. MLMVN With Soft Margins Learning
Author(s): I. Aizenberg
Page(s): 1632-1644

6. Modified Multivalued Neuron With Periodic Tolerant Activation Function
Author(s): J.-P. Chen and S.-J. Lee
Page(s): 1645-1658

7. A Metacognitive Complex-Valued Interval Type-2 Fuzzy Inference System
Author(s): K. Subramanian, R. Savitha, and S. Suresh
Page(s): 1659-1672

8. Complex-Valued B-Spline Neural Networks for Modeling and Inverting Hammerstein Systems
Author(s): S. Chen, X. Hong, J. Gao, and C. J. Harris
Page(s): 1673-1685

9. Fading Channel Prediction Based on Combination of Complex-Valued Neural Networks and Chirp Z-Transform
Author(s): T. Ding and A. Hirose
Page(s): 1686-1695

10. On the Correction of Anomalous Phase Oscillation in Entanglement Witnesses Using Quantum Neural Networks
Author(s): E. C. Behrman, R. E. F. Bonde, J. E. Steck, and J. F. Behrman
Page(s): 1696-1703

SPECIAL ISSUE BRIEF PAPERS

11. Global Stability Criterion for Delayed Complex-Valued Recurrent Neural Networks
Author(s): Z. Zhang, C. Lin, and B. Chen
Page(s): 1704-1708

12. Further Investigate the Stability of Complex-Valued Recurrent Neural Networks With Time-Delays
Author(s): T. Fang and J. Sun
Page(s): 1709-1713

13. Threshold Complex-Valued Neural Associative Memory
Author(s): P. Zheng
Page(s): 1714-1718

14. Principal Component Analysis With Complex Kernel: The Widely Linear Model
Author(s): A. Papaioannou and S. Zafeiriou
Page(s): 1719-1726

15. Ultrawideband Direction-of-Arrival Estimation Using Complex-Valued Spatiotemporal Neural Networks
Author(s): K. Terabayashi, R. Natsuaki, and A. Hirose
Page(s): 1727-1732

16. Adaptive Dynamic Programming for a Class of Complex-Valued Nonlinear Systems
Author(s): R. Song, W. Xiao, H. Zhang, and C. Sun
Page(s): 1733

IEEE Transactions on Evolutionary Computation, Volume 18, Number 4, August 2014

1. Reusing Building Blocks of Extracted Knowledge to Solve Complex, Large-Scale Boolean Problems
Author(s): M. Iqbal, W. N. Browne, and M. Zhang
Pages: 465-480

2. Quick Hypervolume
Author(s): L. M. S. Russo and A. P. Francisco
Pages: 481-502

3. Ant Colony Optimization for Mixed-Variable Optimization Problems
Author(s): T. Liao, K. Socha, M. A. Montes de Oca, T. Stutzle, and M. Dorigo
Pages: 503-518

4. Multiobjective Estimation of Distribution Algorithm Based on Joint Modeling of Objectives and Variables
Author(s): H. Karshenas, R. Santana, C. Bielza, and P. Larranaga
Pages: 519-542

5. Evolving an Improved Algorithm for Envelope Reduction Using a Hyper-Heuristic Approach
Author(s): B. Koohestani and R. Poli
Pages: 543-558

6. Evolving Classifiers to Recognize the Movement Characteristics of Parkinson’s Disease Patients
Author(s): M. A. Lones, S. L. Smith, J. E. Alty, S. E. Lacy, K. L. Possin, D. R. S. Jamieson, and A. M. Tyrrell
Pages: 559-576

7. An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints
Author(s): K. Deb and H. Jain
Pages: 577-601

8. An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part II: Handling Constraints and Extending to an Adaptive Approach
Author(s): H. Jain and K. Deb
Pages: 602

IEEE Transactions on Fuzzy Systems, Volume 22, Number 4, August 2014

REGULAR PAPERS

1. Design of Fuzzy-Neural-Network-Inherited Backstepping Control for Robot Manipulator Including Actuator Dynamics
Author(s): R.-J.Wai and R.Muthusamy
Pages: 709-722

2. Rule-Based Cooperative Continuous Ant Colony Optimization to Improve the Accuracy of Fuzzy System Design
Author(s): C.-F.Juang, C.-W. Hung, and C.-H. Hsu
Pages: 723-735

3. A Model-Based Fault Detection and Prognostics Scheme for Takagi–Sugeno Fuzzy Systems
Author(s): B.T. Thumati, M. A. Feinstein, and S. Jagannathan
Pages: 736-748

4. Intuitionistic Fuzzy Analytic Hierarchy Process
Author(s):Z. Xu and H. Liao
Pages: 749-761

5. A Semisupervised Multiagent System Model to Support Consensus-Reaching Processes
Author(s): I. Palomares and L. Martinez
Pages: 762-777
 
6. Context-Dependent Fuzzy Systems With Application to Time-Series Prediction
Author(s): D. T. Ho and J. M. Garibaldi
Pages: 778-790
 
7. Intelligent Control Using the Wavelet Fuzzy CMAC Backstepping Control System for Two-Axis Linear Piezoelectric Ceramic Motor Drive Systems
Author(s): C.-M. Lin and H.-Y. Li
Pages: 791-802

8. Moment Adaptive Fuzzy Control and Residue Compensation
Author(s): T. Tao and S.-F. Su
Pages: 803-816
 
9. The Exponential Stability and Asynchronous Stabilization of a Class of Switched Nonlinear System Via the T–S Fuzzy Model
Author(s): Y. Mao, H. Zhang, and S. Xu
Pages: 817-828
 
10. Cooperative Coevolution for Large-Scale Optimization Based on Kernel Fuzzy Clustering and Variable Trust Region Methods
Author(s): J. Fan, J. Wang, and M. Han
Pages: 829-839
 
11. The Reduction of Interval Type-2 LR Fuzzy Sets
Author(s): C.-L. Chen, S.-C. Chen, and Y.-H. Kuo
Pages: 840-858

12. From Fuzzy Cognitive Maps to Granular Cognitive Maps
Author(s): W. Pedrycz and W. Homenda
Pages: 859-869
 
13. Robust H8 Control for Stochastic T–S Fuzzy Systems via Integral Sliding-Mode Approach
Author(s): Q. Gao, G. Feng, L. Liu, J. Qiu, and Y. Wang
870-881
 
14. Multicriteria Decision-Making With Imprecise Importance Weights
Author(s): R. R. Yager and N. Alajlan
Pages: 882-891
 
15. Simulation of Fuzzy Queueing Systems With a Variable Number of Servers, Arrival Rate, and Service Rate
Author(s): E.Munoz and E. H. Ruspini
Pages: 892-903
 
16. Membership Function Design for Multifactorial Multivariate Data Characterizing and Coding in Human Component System Studies
Author(s): P. Loslever
Pages: 904-918
 
17. OptiFel: A Convergent Heterogeneous Particle Swarm Optimization Algorithm for Takagi–Sugeno Fuzzy Modeling
Author(s): N.J. Cheung, X.-M. Ding, and H.-B. Shen
Pages: 919-933
 
18. The Generalized TP Model Transformation for T–S Fuzzy Model Manipulation and Generalized Stability Verification
Author(s): P. Baranyi
Pages: 934-948
 
19. Hypermatching: Similarity Matching With Extreme Values
Author(s): R. R. Yager and F. E. Petry
Pages: 949-957
 
20. Pythagorean Membership Grades in Multicriteria Decision Making
Author(s): R. R. Yager
Pages: 958-965
 
21. Dual Bipolar Measures of Atanassov’s Intuitionistic Fuzzy Sets
Author(s): L.-H. Chen, and C.-C. Tu
Pages: 966-982
 
22. Generalized Markov Models for Real-Time Modeling of Continuous Systems
Author(s): D. P. Filev and I. Kolmanovsky
Pages: 983-998
 
23. GT2FC: An Online Growing Interval Type-2 Self-Learning Fuzzy Classifier
Author(s): A. Bouchachia and C. Vanaret
Pages: 999-1018

SHORT PAPERS

24. Fuzzy-Model-Based D-Stability and Nonfragile Control for Discrete-Time Descriptor Systems With Multiple Delays
Author(s): F. Li, P. Shi, L. Wu, and X. Zhang
Pages: 1019-1025
 
25. Further Studies on Control Synthesis of Discrete-Time T–S Fuzzy Systems via Useful Matrix Equalities
Author(s): X. Xie, D. Yue, and X. Zhu
Pages: 1026-1030
 
26. Stability Analysis of Positive Interval Type-2 TSK Systems With Application to Energy Markets
Author(s): M.S.  Fadali and S. Jafarzadeh
Pages: 1031-1038
 
27. Orness Measure of OWA Operators: A New Approach
Author(s): A. Kishor, A. K. Singh, and N. R. Pal
Pages: 1039-1044
 
28. A Characterization of the Orthogonal Grid Constructions of Copulas
Author(s): J. F. Sanchez and M. Ubeda-Flores
Pages: 1045

IEEE Transactions on Neural Networks and Learning Systems: Volume 25, Issue 8, August 2014

1. GMM-Based Intermediate Matching Kernel for Classification of Varying Length Patterns of Long Duration Speech Using Support Vector Machines
Authors: Aroor Dinesh Dileep; Chellu Chandra Sekhar
Page(s): 1421 - 1432

2. Extensions of Kmeans-Type Algorithms: A New Clustering Framework by Integrating Intracluster Compactness and Intercluster Separation
Authors: Xiaohui Huang; Yunming Ye; Haijun Zhang
Page(s): 1433 - 1446

3. Efficient Kernel Sparse Coding Via First-Order Smooth Optimization
Authors: Minyoung Kim
Page(s): 1447 - 1459

4. Contact-Force Distribution Optimization and Control for Quadruped Robots Using Both Gradient and Adaptive Neural Networks
Authors: Zhijun Li; Shuzhi Sam Ge; Sibang Liu
Page(s): 1460 - 1473

5. On the Capabilities and Computational Costs of Neuron Models
Authors: Michael J. Skocik; Lyle N. Long
Page(s): 1474 - 1483

6. Global Sensitivity Analysis Approach for Input Selection and System Identification Purposes—A New Framework for Feedforward Neural Networks
Authors: Eric Fock
Page(s): 1484 - 1495

7. Cooperative Tracking Control of Nonlinear Multiagent Systems Using Self-Structuring Neural Networks
Authors: Gang Chen; Yong-Duan Song
Page(s): 1496 - 1507

8. Distributed Neural Network Control for Adaptive Synchronization of Uncertain Dynamical Multiagent Systems
Authors: Zhouhua Peng; Dan Wang; Hongwei Zhang; Gang Sun
Page(s): 1508 - 1519

9. Instance-Level Constraint-Based Semisupervised Learning With Imposed Space-Partitioning
Authors: Jayaram Raghuram; David J. Miller; George Kesidis
Page(s): 1520 - 1537

10. Modified Principal Component Analysis: An Integration of Multiple Similarity Subspace Models
Authors: Zizhu Fan; Yong Xu; Wangmeng Zuo; Jian Yang; Jinhui Tang; Zhihui Lai; David Zhang
Page(s): 1538 - 1552

11. On the Complexity of Neural Network Classifiers: A Comparison Between Shallow and Deep Architectures
Authors: Monica Bianchini; Franco Scarselli
Page(s): 1553 - 1565

12. A Minimum Resource Neural Network Framework for Solving Multiconstraint Shortest Path Problems
Authors: Junying Zhang; Xiaoxue Zhao; Xiaotao He
Page(s): 1566 - 1582

13. Simulating Dynamic Plastic Continuous Neural Networks by Finite Elements
Authors: Abdolreza Joghataie; Omid Oliyan Torghabehi
Page(s): 1583 - 1587

14. Minimizing Nearest Neighbor Classification Error for Nonparametric Dimension Reduction
Authors: Wei Bian; Tianyi Zhou; Aleix M. Martinez; George Baciu; Dacheng Tao
Page(s): 1588 - 1594

15. Correction to “Convergence and Rate Analysis of Neural Networks for Sparse Approximation”
Authors: Aurele Balavoine; Justin Romberg; Christopher J. Rozell
Page(s): 1595 - 1596

IEEE TNNLS Special issue on “Neurodynamic Systems for Optimization and Applications”

Recurrent neural networks, as dynamical systems, are usually used as models for solving computationally intensive problems. Because of their inherent nature of parallel and distributed information processing, recurrent neural networks are promising computational models for real-time applications. Constrained optimization problems arise in a wide variety of scientific and engineering applications, including signal and image processing, system identification, robot control, process control, pattern recognition, etc. Since the Hopfield neural network was introduced for solving optimization problems, significant progress has been made in theory, algorithms and applications. A number of neurodynamic models have been proposed for solving different problems ranging from discrete optimization to continuous optimization, linear programming to nonlinear optimization, convex optimization to non-convex optimization, smooth optimization to non-smooth optimization, numerical software to analog hardware implementations, etc. Some of them have been successfully applied to robot control, process control, signal and image processing, pattern recognition and classification, economic prediction and so on. In addition, as a kind of neuromorphic systems, they are potentially useful for simulating the brain functions, which is an important topic in neuroscience.

The objective of this special issue is to bring together recent advances in the field of neurodynamic systems for solving optimization problems. We invite original and unpublished research contributions in all relevant areas. We will encourage submissions of papers with new models and applications which would further promote research activities in this area.

Topics of interest include, but are not limited to:

• Neurodynamic models for constrained optimization
• Neurodynamic models for multi-objective optimization
• Neurodynamic models for large-scale optimization problems
• Neurodynamic models for deep learning
• Neurodynamic models for optimal control
• Neurodynamic models for tensor decomposition
• Analysis of neurodynamic optimization systems
• Neurodynamic optimization in the brain
• Neurodynamic optimization for process control
• Neurodynamic optimization for robot control
• Neurodynamic optimization for biomedical engineering problems
• Neurodynamic optimization for signal processing
• Neurodynamic optimization for image processing
• Neurodynamic optimization for support vector machine learning
• Neurodynamic optimization for pattern recognition
• Neurodynamic optimization for other applications

IMPORTANT DATES

Aug. 15, 2014 – Deadline for manuscript submission
Dec. 31, 2014 – Notification to authors
Feb. 15, 2015 – Deadline for submission of revised manuscripts
Mar.1, 2015 – Final decision
May/June 2015 – Special issue publication in the IEEE TNNLS.

SUBMISSION INSTRUCTIONS

1. Read the information for authors at http://cis.ieee.org/tnnls
2. Submit the manuscript by August 15, 2014 at the IEEE-TNNLS webpage http://mc.manuscriptcentral.com/tnnls and follow the submission procedure. Please indicate clearly on the first page of the manuscript and the Author’s Cover Letter that the manuscript has been submitted to the Special Issue on Neurodynamic Systems for Optimization and Applications. Send also an e-mail to chenglong@compsys.ia.ac.cn with subject “TNNLS special issue submission” to notify the editors of your submission.

GUEST EDITORS

Zhigang Zeng
Huazhong University of Science and Technology, China
zgzeng@hust.edu.cn
http://auto.hust.edu.cn/zhigangzeng/

Andrzej Cichocki
Brain Science Institute, RIKEN, Japan
cia@braiin.riken.jp
http://www.bsp.brain.riken.jp/~cia/

Long Cheng
Institute of Automation, Chinese Academy of Sciences, China
long.cheng@ia.ac.cn
http://compsys.ia.ac.cn/~chenglong

Yousheng Xia
Fuzhou University, China
ysxia@fzu.edu.cn
http://cmcs.fzu.edu.cn/action-model-name-teacher-itemid-34

Xiaolin Hu
Tsinghua University, China
xlhu@tsinghua.edu.cn
www.xlhu.cn

WCCI 2014 Day 4

The social media subcommittee has been active again on day four of WCCI 2014, tweeting sessions and making notes for today's post.

The first event was a panel session on Big Data and Computational Intelligence, chaired by Jerry Mendel. Jerry gave an overview of big data, and called for innovative approaches to solve big data problems.

Jose Lazano made the point that big data problems are the same as we have been solving in computational intelligence for years, but that the approaches have to be different. He described the characteristics of big data as the three Vs: Volume, as in the scale of the data; Velocity, the speed the data arrives; and Variety, the wide scope of what the data represents.

Nitesh Chawla added a fourth V, Veracity. How much confidence do we have in the data and its value? He also noted that while companies like Facebook have no problems getting big data sets, it is difficult for academics. Tim Havens echoed this, adding that there is a need for good benchmark data sets for big data. He also pointed out that there are always trade-offs how you validate algorithms for big data.

Xiaodong Li gave a brief overview of the computational intelligence techniques for big data. He especially listed deep learning, parallelized machines and robustness techniques for dealing with volume, and online learning methods for dealing with velocity. He also gave an excellent definition of big data: if you can fit it into memory, it's not big data.

The last speaker was Yaochu Jin, who pointed out that due to its volume and variety, biological data like microarray data and gene regulatory networks is also big data.

Janusz Kacprzyk gave an invited lecture on 'Fuzzy dynamic programming: a step towards cognitive dynamic programming'. Janusz presented fuzzy dynamic systems modelling of government regional planning over multiple years for improving cognitive perceptions of socio-economic problems and quality of life. Janusz's passion for this shone through as he stated clearly that this is a real fuzzy model, for a real and important problem, for real end users, and for real money. The model contained fuzzy goals and fuzzy constraints that are objective, such as government limits, but also domain knowledge from experts that are subjective, such as seven life quality indicators.

Huaguang Zhang gave an invited lecture on 'Fuzzy Real-time Leakage Supervisory System for Fluid Transportation Pipeline Networks: New Methods and Applications'. Huaguang research focused on identifying weak leakage in long-distance petroleum pipelines the transient flow produces a drop in pressure at the leakage point of 1%, which is a challenging task. The importance of identifying weak leakages was demonstrated with recent loss of life and economic loses estimated to be 4.4 billion yuan RMB. The first stage of Huanguang's system filters noise signals but not the leakage signals. The second stage validated each characteristic of a chaotic system with statistical analysis. The third stage modelled sections of pipe and raised alarms when differences in sections over time met a threshold. The fourth stage modelled the operating model with the generalised fuzzy hyperbolic tangent model.