IEEE Transactions on Neural Networks and Learning Systems: Volume 25, Issue 5, May 2014

1. Classification in the Presence of Label Noise: A Survey
Author(s): Benoit Frenay and Michel Verleysen
Pages: 845 - 869

2. Efficient Algorithms for Exact Inference in Sequence Labeling SVMs
Author(s): Alexander Bauer; Nico Gornitz; Franziska Biegler; Klaus-Robert Muller; Marius Kloft
Pages: 870 - 881

3. Robust Adaptive Dynamic Programming and Feedback Stabilization of Nonlinear Systems
Author(s): Yu Jiang; Zhong-Ping Jiang
Pages: 882 - 893

4. A Spiking Self-Organizing Map Combining STDP, Oscillations, and Continuous Learning
Author(s): Timothy Rumbell; Susan L. Denham; Thomas Wennekers
Pages: 894 - 907

5. An Online Outlier Identification and Removal Scheme for Improving Fault Detection Performance
Author(s): Hasan Ferdowsi; Sarangapani Jagannathan; Maciej Zawodniok
Pages: 908 - 919

6. Fidelity-Based Probabilistic Q-Learning for Control of Quantum Systems
Author(s): Chunlin Chen; Daoyi Dong; Han-Xiong Li; Jian Chu; Tzyh-Jong Tarn
Pages: 920 - 933

7. On the Impact of Approximate Computation in an Analog DeSTIN Architecture
Author(s): Steven Young; Junjie Lu; Jeremy Holleman; Itamar Arel
Pages: 934 - 946

8. Adaptive Neural Tracking Control for a Class of Nonstrict-Feedback Stochastic Nonlinear Systems With Unknown Backlash-Like Hysteresis
Author(s): Huanqing Wang; Bing Chen; Kefu Liu; Xiaoping Liu; Chong Lin
Pages: 947 - 958

9. Simplified Interval Type-2 Fuzzy Neural Networks
Author(s): Yang-Yin Lin; Shih-Hui Liao; Jyh-Yeong Chang; Chin-Teng Lin
Pages: 959 - 969

10. Modeling of Batch Processes Using Explicitly Time-Dependent Artificial Neural Networks
Author(s): Botla Ganesh; Vadlagattu Varun Kumar; Kalipatnapu Yamuna Rani
Pages: 970 - 979

11. Storing Sparse Messages in Networks of Neural Cliques
Author(s): Behrooz Kamary Aliabadi; Claude Berrou; Vincent Gripon; Xiaoran Jiang
Pages: 980 - 989

12. Incipient Interturn Fault Diagnosis in Induction Machines Using an Analytic Wavelet-Based Optimized Bayesian Inference
Author(s): Jeevanand Seshadrinath; Bhim Singh; Bijaya Ketan Panigrahi
Pages: 990 - 1001

13. A Scalable Stagewise Approach to Large-Margin Multiclass Loss-Based Boosting
Author(s): Sakrapee Paisitkriangkrai; Chunhua Shen; Anton van den Hengel
Pages: 1002 - 1013

14. Data-Driven MFAC for a Class of Discrete-Time Nonlinear Systems With RBFNN
Author(s): Yuanming Zhu; Zhongsheng Hou
Pages: 1013 - 1020

Call for Papers: Special Issue on Real-Time Strategy Games IEEE TCIAIG

Special issue editors: Michael Buro, Santiago Ontañón and Mike Preuss

In recent years game AI for real-time strategy (RTS) games has become an active research area. Producing AI players (bots) which are able to consistently beat even average human players (without cheating) in these games has risen as a real challenge. Thus, in RTS games, player satisfaction cannot simply be achieved by “downgrading” the AI, as is possible in man other game genres. In consequence, stronger AI players make the game more interesting.

Recent RTS AI (e.g. StarCraft) tournaments have stimulated the creation of new bots with new concepts and architectures and led to a greatly increased number of publications addressing some of the many open AI problems in RTS games. For example, RTS game aspects such as resource management, scouting, real-time strategic and tactical planning, and others, call for the application of innovative CI/AI methods. This special issue welcomes high-quality work in the area of real-time strategy games. Topics include but are not limited to:

• Adversarial real-time planning in RTS games
• Bot reactiveness: learning and adaptation in RTS bots
• Build order optimization and its relation to strategies and the metagame
• Scouting and uncertainty management in RTS games
• Path-finding and group movement
• Combat simulation and AI for micro-management
• Opponent modeling, especially strategy prediction
• Complexity measurements for RTS games
• Communication and cooperation with and within RTS bots
• New forms of interaction with the player
• AI adaptations for more satisfying play experience
• Difficulty adaptation, ability-based matching, ladders, and tournaments
• Automated level/unit/map design for RTS games
• Multiplayer online battle arena (MOBA) games: the next generation of real-time strategy?

Authors should follow normal T-CIAIG guidelines for their submissions, but clearly identify their papers for this special issue during the submission process. See http://csee.essex.ac.uk/tciaig/authors.html for author information. Extended versions of previously published conference/workshop papers are welcome providing the journal paper is a significant extension of the conference paper, and is accompanied by a covering letter explaining the additional contribution.

Deadline for submissions: July 1, 2014 Final copy due: February 1, 2015
Notification of Acceptance: November 1, 2014 Publication: June 2015

IEEE Computational Intelligence Magazine Volume 9 Issue 2 May 2014

1. What Is Your Main IEEE Society? [Editor's Remarks]
Author(s): Ishibuchi, H.

2. President's Greeting [President's Message]
Author(s): Yao, X.

3. CIS Society Officers

4. Newly Elected CIS Administrative Committee Members (2014-2016) [Society Briefs]
Author(s): Yao, X.

5. IEEE Fellows - Class of 2014 [Society Briefs]
Author(s): Bezdek, J.

6. A Report on the CIS Second Video Competition [Society Briefs]
Author(s): Matthews, S. ; Abdool, A. ; Eliades, D. ; Coyle, D. ; Posada, J. ; Martin, E. ; Sperduti, A. ; Alippi, C. ; Estevez, P.

7. CIS Publication Spotlight
Author(s): Liu, D. ; Lin, C. ; Greenwood, G. ; Lucas, S. ; Zhang, Z.

8. Special Issue on Computational Intelligence for Community-Centric Systems [Guest Editorial]
Author(s): Kubota, N. ; Liu, H.

9. Context-Aware Personal Information Retrieval From Multiple Social Networks
Author(s): Han, X. ; Wei, W. ; Miao, C. ; Mei, J. ; Song, H.

10. Landmark-Based Methods for Temporal Alignment of Human Motions
Author(s): de Dios, P. ; Chung, P. ; Meng, Q.

11. Muscle Fatigue Tracking with Evoked EMG via Recurrent Neural Network: Toward Personalized Neuroprosthetics
Author(s): Li, Z. ; Hayashibe, M. ; Fattal, C. ; Guiraud, D.

12. Jumping NLP Curves: A Review of Natural Language Processing Research [Review Article]
Author(s): Cambria, E. ; White, B.

13. A Memetic Algorithm for Resource Allocation Problem Based on Node-Weighted Graphs [Application Notes]
Author(s): Wu, J. ; Chang, Z. ; Yuan, L. ; Hou, Y. ; Gong, M.

14. Conference Calendar
Author(s): Haddow, P.

15. Call for Papers for Journal Special Issues

16. CEC 2015

Call for Special Session Proposals for IEEE SSCI 2014

The IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2014) invites Special Session proposals for our December 9 - 12, 2014 conference in Orlando, Florida, USA. Special session proposals can be for any of the symposium under the IEEE SSCI 2014 umbrella. Special Session proposals should include the following:

• A brief description, rationale or motivation of the proposed session
• The title of the proposed special session, and the specific symposium under which the special session should be listed
• List of topics and the scope
• A list of authors who have already been invited to participate (if any)
• Short bio information of the SS organizers

Please submit the SS proposals to the corresponding Symposium Chairs (Symposium Chairs can approve their SS directly, see list of Symposia at http://www.ieee-ssci.org/glance.html), or to the SSCI 2014 Special Session Chair, Robi Polikar at polikar@rowan.edu, with a copy to Haibo He at he@ele.uri.edu. More details can be found at http://www.ieee-ssci.org.

The deadline for special session proposals is April 15, 2014.

IEEE TNLS Call for Papers: 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 Aug 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