Call for Papers: WCCI 2014 Special Session "Advanced Computational Intelligence for Algorithmic Trading"

Motivation:

There is an explosive growth of Algorithmic Trading (i.e., Algo Trading, Program Trading, or Automated Trading) in research and practice over the past few years. As the markets are evolving fast, numerous problems have arisen in the field of Algorithmic Trading due to many reasons. Just a few of them are mentioned below.

First of all, as high volume and high variety heterogeneous data at different frequency, e.g., high and even ultra-high frequency, are exploding in the market, the modeling tasks to explore the big information across from structure to non-structure data have become increasingly complex. Second, with different policy manipulation of market, the market regimes have changed fast in volatility. The demand to achieve robust strategies has even been strong. Finally, as shown in the work by 2013 Nobel laureates, Fama and Shiller, the market is a changing mixture of efficiency and “irrational exuberance”.  It has been interesting and extremely challenging for an investment organization to balance the investment horizons for its portfolio and select different trading strategies to explore and exploit inefficiency and irrationality in the market and then make profits and control risks.

All these kinds of problems attract professionals and researchers to intensively study novel methodology with advanced tools. In many cases, conventional mathematical approaches do not well support the automated trading. Computational/artificial intelligence has the power in adaptively learning the models from data, inferring the market states upon the new information and naturally accounting for the uncertainty. We believe that advanced computational intelligence approaches will mitigate and even someday solve the existing and emerging problems in our trading practice by helping us build up intelligent trading agents.

Goal:

This special session is an approved plan by IEEE CIS Computational Finance and Economics Technical Committee (CFETC) .

It aims to bring practical pioneers and academic researcher together, provide an idea exchanging environment, and explore potential collaboration between industry and academia. In this session, we will explore new theories and solve real trading problems. We also hope to re-exam the Algorithmic Trading state-of-the-art and paradigms under recent data complexity development and policy risks.

Topics:

Topics of interests include, but not limited to, trading models and strategies, risk management, pricing for algorithmic trading, and strategy validation for different underlines in different markets, as follows:

• Connectionist approaches, e.g., neural networks, for learning and approximating price-related functions, market prediction and other novel applications.
• Deep and shallow learning of market structure.
• Bayesian approaches for modeling market factors.
• Non-parametric statistical approaches for trading activities.
• Latent and hidden structure methods for market regime and state identification.
• Transfer learning for information borrowing from different markets and assets.
• Adaptive learning/control for achieving robust strategies.
• Reinforcement learning paradigm for handling and decision-making under high uncertainty.
• Agent-based models and their applications in artificial market and other directions.
• Behavior-based approaches for understanding market inefficiency and irrationality.
• Trading models/agents and strategies evolution.
• Fuzzy system combination with other approaches for inference and decision-making.
• Information theoretic methods and other approaches for portfolio optimization and for optimally allocating capital between trading strategies.
• Utilization of non-structure data and big data in trading practice.
• Validation of algorithmic trading models and strategies.
• Better understanding and control of risk during trading with advanced intelligent modeling.

Information for Authors

This section is part of IEEE International Joint Conference on Neural Network 2014 (IEEE IJCNN 2014) at The IEEE World Congress on Computational Intelligence 2014 (IEEE WCCI 2014).
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.php
3)      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

Important dates

Paper submission: 20 December, 2013
Decision: 15 March, 2014
Final paper submission: 15 April, 2014
Conference dates: 6-11 July, 2014

Organizers:

Dr. Chenghui Cai
Global Market Department, Opera Solutions LLC, New York/New Jersey.
E-mail: caichenghui@gmail.com

Dr. Ming Li
Taikang Asset Management Company, Beijing and Taikang Financial Engineering Department.

Dr. Meng Ji
Ernst & Young, New York City.

Prof. Akira Namatame
Department of Computer Science, the Japan Defense Academy (NDA).

Prof. Philip Yu
Department of Statistics and Actuarial Science of The University of Hong Kong.

Prof. Kiyoshi Izumi
School of Engineering, The University of Tokyo

Mr. Robert Golan
DBmind Technologies, USA.

Call for papers: WCCI 2014 Special Session "Complex Networks and Evolutionary Computation"

Introduction to the special session

The application of complex networks to evolutionary computation (EC) has received considerable attention from the EC community in recent years. The most well-known study should be the attempt of using complex networks, such as small-world networks and scale-free networks, as the potential population structures in evolutionary algorithms (EAs). Moreover, the study of using complex networks to analyse fitness landscapes and designing predictive problem difficulty measures is also attracting increasing attentions. On the other hand, using EAs to solve problems related to complex networks, such as community detection, is also a popular topic.

This special session seeks to bring together the researchers from around the globe for a creative discussion on recent advances and challenges in combining complex networks and EAs. The special session will focus on, but not limited to, the following topics:

• Complex networks and fitness landscape analysis
• Complex networks and problem difficulty prediction
• Evolutionary dynamics on complex networks
• Evolutionary algorithms based on complex networks
• Community detection using evolutionary algorithms
• Community detection using multi-objective evolutionary algorithms
• Real world applications of evolutionary algorithms based on complex networks

Submission Guidelines

The authors intended to contribute to IEEE WCCI 2014 Special Sessions are kindly recommended to follow the manuscript style information and templates of regular IEEE WCCI 2014 papers. Please note that each Special Session is specifically and exclusively related to one of the three conferences composing the IEEE WCCI 2014, i.e., IJCNN2014, FUZZ-IEEE2014, and IEEE CEC 2014.
When submitting their manuscripts, authors are recommended to follow these steps:
1. Identify the conference associated to the Special Session they are interested in, by looking at the "Provisionally Accepted Special Session" list under the column called ID;
2. Go to the related conference submission website;
3. Select the Special Session name in the Main Research topic dropdown list (our Special Session name is Evolutionary Computation for Planning and Scheduling);
4. Fill out the input fields, upload the pdf file and finalize the submission by December 20, 2013.

Organizers

Prof. Jing Liu
Affiliation: Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, China
Email: neouma@mail.xidian.edu.cn
Home page: http://see.xidian.edu.cn/faculty/liujing/

Call for papers: WCCI 2014 Special Session "Fuzzy Set Theory in Computer Vision"

Fuzzy set theory is the subject of intense investigation in fields like control theory, robotics, biomedical engineering, computing with words, knowledge discovery, remote sensing and socioeconomics. However, in the area of computer vision, other fields, e.g., machine learning, and communities, e.g., PAMI, ICCV, CVPR, ECCV, NIPS, are arguably the state-of-the-art. In particular, the vast majority of top performing techniques on public datasets are steeped in probability theory. Important questions to the fuzzy set community include the following. What is the role of fuzzy set theory in computer vision? Does fuzzy set theory make the biggest impact in terms of low-, mid- or high-level computer vision? Furthermore, do current performance measures favor machine learning approaches? Is there additional benefit that fuzzy set theory brings, and if so, how is it measured?

This special session invites new research in fuzzy set theory in computer vision. It is a follow up to the 2013 FUZZ-IEEE workshop View of Computer Vision Research and Challenges for the Fuzzy Set Community. In particular, we encourage authors to investigate their research using public datasets and to compare their results to both fuzzy and non-fuzzy methods. Topics of interest include all areas in computer vision and image/video understanding. Example topics include, but are not limited to, the following:

• Detection and recognition
• Categorization, classification, indexing and matching
• 3D-based computer vision
• Advanced image features and descriptors
• Motion analysis and tracking 
• Linguistic description and summarization
• Video: events, activities and surveillance
• Intelligent change detection
• Face and gesture
• Low-level, mid-level and high-level computer vision
• Data fusion for computer vision
• Medical and biological image analysis
• Vision for Robotics

Interested authors intended to contribute to this special sessions are kindly recommended to follow the manuscript style information and templates of regular IEEE WCCI 2014 papers, as described in here. When submitting the manuscripts, authors are recommended to follow these steps:
1.    Select this Special Session name (FZ 13 – Fuzzy Set Theory in Computer Vision) in the Main Research topic dropdown list;
2.    Fill out the input fields, upload the pdf file and finalize the submission by December 20, 2013.

Organizers:

Chee Seng Chan (Uni. of Malaya)                   
James Keller  (Uni. of Missouri)
Derek T Anderson (Mississippi State Uni.)      
Tony Xu Han (Uni. of Missouri)

Call for papers: WCCI 2014 Special Session "Lattice Computing"

Aim and scope

During the last ten years, a novel analysis of fuzzy intervals based on lattice theory has paved the way for novel extensions of major computational intelligence paradigms including the: fuzzy inference systems, fuzzy adaptive resonance theory and self-organizing maps. Novelties include 1) accommodation, in principle, of granular system inputs, 2) computing with words, and 3) introduction of tunable nonlinearities. Evolutionary computation is often employed for tuning performance.

In addition, lattice theory is also used instrumentally in different domains including logic as well as reasoning. Likewise, lattice theory is used in mathematical morphology toward signal processing as well as in formal concept analysis toward knowledge-representation. Lately, the term Lattice Computing, or LC for short, has been introduced as “an evolving collection of tools and methodologies that process lattice-ordered data including logic values, numbers, sets, symbols, graphs, etc”. In the aforementioned sense, LC emerges with the potential of unifying rigorously the treatment of disparate types of data either separately or jointly in any combination.

This special session is meant as a forum for researchers with interests in LC. The objective is to present high-quality, state-of-the-art research results. An array of novel mathematical tools, design practices and real world applications will be presented. Emphasis is on reasoning, knowledge-representation, signal processing, system modeling, clustering, classification and the cross-fertilization of different technologies. Topics of interest include but are not limited to

• Lattice algebra neural networks 
• Fuzzy lattice reasoning 
• Fuzzy adaptive resonance theory 
• Mathematical morphology 
• Implications 
• Similarity measures 
• System modeling 
• Probabilistic reasoning 
• Granular computing
• Computing with words
• Data mining
• Disparate data fusion
• Semantic Web
• Knowledge representation
• Formal concept analysis
• Algebraic logic
• Multi-valued logic
• Spatial and temporal logic
• Automated reasoning
• Application of proof theory
• Pattern recognition

 

Organizers

Professor Vassilis KABURLASOS
Department of Computer & Informatics Engineering
TEI of Eastern Macedonia & Thrace
Agios Loukas 65404 Kavala, Greece
Email: vgkabs@teikav.edu.gr
His “google scholar” WebPage is at http://scholar.google.com/citations?user=3RiPf3wAAAAJ

Professor Manuel GRAÑA
Department of Computer Science and Artificial Intelligence
Universidad del Pais Vasco (UPV/EHU)
Paseo Manuel Lardizabal 1, 20018 Donostia-San Sebastian, Spain
Email: manuel.grana@ehu.es
His “google scholar” WebPage is at http://scholar.google.com/citations?hl=es&user=lM0Hb4wAAAAJ

Professor Yang XU
College of Mathematics
Southwest Jiaotong University
Chengdu 610031, Sichuan, P. R. China
Email: xuyang@home.swjtu.edu.cn

Call for papers: WCCi 2014 Special Session "Data Mining and Machine Learning Meet Evolutionary Computation"

Introduction to the special session:

Evolutionary Computation (EC), such as Genetic Algorithm(GA), Genetic Programming(GP) , Particle Swarm Optimization(PSO) and the like, has been widely applied to many aspects in the fields of data mining and machine learning, mostly as an optimization technique. On the other hand, EC is a class of population-based iterative algorithms, which generate abundant data about the search space, problem feature and population information during the optimization process. Therefore, the data mining and machine learning techniques can also be used to analyze these data for improving the performance of EC. A plethora of successful applications have been reported in these two aspects, yet, there remain many open issues and opportunities that are continually emerging as intriguing challenges for the field. The aim of this special session is to serve as a forum for scientists in this field to exchange the latest advantages in theories, technologies, and practice.

We invite researchers to submit their original and unpublished work related to, but not limited to, the following topics:

• EC Enhanced by Data Mining and Machine Learning Concepts and/or Method
• Data Mining and Machine Learning Based on EC techniques    
• Data Mining and Machine Learning Enhanced Multi-Objective Optimization     
• Data Mining and Machine Learning Enhanced Constrained Optimization:     
• Data Mining and Machine Learning Enhanced Memetic Computation:
• Multi-Objective Optimization and Rule Mining Problems
• Knowledge Discovery in Data Mining via Evolutionary Algorithm
• Genetic Programming in Data Mining
• Multi-Agent Data Mining using Evolutionary Compuation
• Medical Data Mining with Evolutionary Computation
• Evolutionary Computation in Intelligent Network Management
• Evolutionary Clustering in Noisy Data Sets
• Big Data Projects with Evolutionary Computation
• Real World Applications   

Paper Submission:

All papers should be submitted electronically through:
http://ieee-cis.org/conferences/cec2014/upload.php

To submit your papers to the MC special session, please select the Special Session name in the Main Research topic.

For more submission information please visit: http://www.ieee-wcci2014.org/Paper%20Submission.htm
All accepted papers will be published in the WCCI 2014 electronic proceedings, included in the IEEE Xplore digital library, and indexed by  EI Compendex.

Co-Organizers

Zhun Fan, Department of Electronic Engineering, Shantou University, Shantou, China
E-mail: zfan@stu.edu.cn
Zhun Fan received his Ph.D. (Electrical and Computer Engineering) in 2004 from the Michigan State University. He received the B.S. degree in 1995 and M.S degree in 2000, both from Huazhong University of Science and Technology, China. From 2004 to 2011, he was employed as an Assistant Professor and Associate Professor at the Technical University of Denmark. He has also been working at the BEACON Center for Study of Evolution in Action at Michigan State University. He is currently a Professor at the Shantou University, China.  His major research interests include applying evolutionary computation and computational intelligence in design automation and optimization of mechatronic systems, computational intelligence, wireless communication networks, MEMS, intelligent control and robotic systems, robot vision etc

Xinye Cai
Nanjing University of Aeronautics and Astronautics, Nanjing, China
E-Mail: xinye@nauu.edu.cn
Xinye Cai received his BEng. Degree in Electronic&Information Engineering Department from Huazhong Univeristy of Science&Technology, China in 2004, and a Msc. degree in Electronic Department University of York, UK in 2006. Later, he received his PhD degree in Electrical&Computer Engineering Department in Kansas State University in 2009. Currently, he is a lecturer with College of Computer Science and Technology, Nanjing University of Aeronautics&Astronautics. His main research interests include evolutionary computation, multi-objective optimization, constrained optimization and relevant real-world application.


Jun Zhang
Sun Yat-Sen University, Guangzhou, China.
E-Mail: issai@mail.sysu.edu.cn
Jun Zhang (M’02–SM’08) received the Ph.D. degree in electrical engineering from the City University of Hong Kong, Kowloon, Hong Kong, in 2002. From 2003 to 2004, he was a Brain Korean 21 Post-Doctoral Fellow with the Department of Electrical Engineering and Computer Science, Korea Advanced Institute of Science and Technology, Daejeon, Korea. Since 2004, he has been with Sun Yat-Sen University, Guangzhou, China, where he is currently a Cheung Kong Professor with the Department of Computer Science. He has authored seven research books and book chapters, and over 100 technical papers in his research areas. His current research interests include computational intelligence, cloud computing, high performance computing, data mining, wireless sensor networks, operations research, and power electronic circuits. Dr. Zhang was a recipient of the China National Funds for Distinguished Young Scientists from the National Natural Science Foundation of China in 2011 and the First-Grade Award in Natural Science Research from the Ministry of Education, China, in 2009. He is currently an Associate Editor of the IEEE Transactions on Evolutionary Computation, the IEEE Transactions on Industrial Electronics, the IEEE Transactions on Cybernetics, and the IEEE Computational Intelligence Magazine. He is the Founding and Current Chair of the IEEE Guangzhou Subsection and IEEE Beijing (Guangzhou) Section Computational Intelligence Society Chapters.

K. C. Tan
Department of Electrical and Computer Engineering, National University of Singapore, Singapore
Mail: eletankc@nus.edu.sg
TAN Kay Chen received the B. Eng degree with First Class Honors in Electronics and Electrical Engineering, and the Ph.D. degree from the University of Glasgow, Scotland, in 1994 and 1997, respectively. He is actively pursuing research in computational and artificial intelligence, with applications to multi-objective optimization, scheduling, automation, data mining, and games. Dr Tan has published over 100 journal papers, over 100 papers in conference proceedings, co-authored 5 books including Multiobjective Evolutionary Algorithms and Applications (Springer-Verlag, 2005), Modern Industrial Automation Software Design (John Wiley, 2006; Chinese Edition, 2008), Evolutionary Robotics: From Algorithms to Implementations (World Scientific, 2006; Review), Neural Networks: Computational Models and Applications (Springer-Verlag, 2007), and Evolutionary Multi-objective Optimization in Uncertain Environments: Issues and Algorithms (Springer-Verlag, 2009), co-edited 4 books including Recent Advances in Simulated Evolution and Learning (World Scientific, 2004), Evolutionary Scheduling (Springer-Verlag, 2007), Multiobjective Memetic Algorithms (Springer-Verlag, 2009), and Design and Control of Intelligent Robotic Systems (Springer-Verlag, 2009). Dr Tan has been invited to be an invited keynote/plenary speaker for over 30 international conferences. He served in the international program committee for over 100 conferences and involved in the organizing committee for over 40 international conferences, including the General Co-Chair for IEEE Congress on Evolutionary Computation 2007 in Singapore. Dr Tan is the General Co-Chair for IEEE World Congress on Computational Intelligence 2016 in Vancouver, Canada. Dr Tan is an IEEE Distinguished Lecturer of IEEE Computational Intelligence Society since 2011. Dr Tan is currently the Editor-in-Chief of IEEE Computational Intelligence Magazine (CIM). He also serves as an Associate Editor / Editorial Board member of over 20 international journals, such as IEEE Transactions on Evolutionary Computation, IEEE Transactions on Cybernetics, IEEE Transactions on Computational  Intelligence and AI in Games, Evolutionary Computation (MIT Press), European Journal of Operational Research, Journal of Scheduling etc. Dr Tan is the awardee of the 2012 IEEE Computational Intelligence Society (CIS) Outstanding Early Career Award for his contributions to evolutionary computation in multi-objective optimization. He also received the Recognition Award (2008) from the International Network for Engineering Education & Research (iNEER) for his outstanding contributions to engineering education and research.

Qingfu Zhang
School of Computer Science & Electronic Engineering,
University of Essex, Essex, UK
E-Mail: qzhang@essex.ac.uk
Qingfu Zhang is currently a Professor with the School of Computer Science and Electronic Engineering, University of Essex, UK. His is also a Changjiang Visiting Chair Professor in Xidian University, China. From 1994 to 2000, he was with the National Laboratory of Parallel Processing and Computing, National University of Defence Science and Technology, China, Hong Kong Polytechnic University, Hong Kong, the German National Research Centre for Information Technology (now Fraunhofer-Gesellschaft, Germany), and the University of Manchester Institute of Science and Technology, Manchester, U.K. He holds two patents and is the author of many research publications. His main research interests include evolutionary computation, optimization, neural networks, data analysis, and their applications. Dr. Zhang is an Associate Editor of the IEEE Transactions on Evolutionary Computation and the IEEE Transactions on Systems, Man, and Cybernetics–Part B. He is also an Editorial Board Member of three other international journals.  MOEA/D, a multiobjective optimization algorithm developed in his group, won the Unconstrained Multiobjective Optimization Algorithm Competition at the Congress of Evolutionary Computation 2009, and was awarded the 2010 IEEE Transactions on Evolutionary Computation Outstanding Paper Award.

Call for papers: WCCI 2014 Special Session "Evolutionary Multi-objective Optimization"

Organizers:

Sanaz Mostaghim, University of Magdeburg, Germany sanaz.mostaghim@ovgu.de
Kalyanmoy Deb, Michigan State University, USA, kdeb@msu.edu

Scope:

This special session invites papers discussing recent advances in the development and application of biologically-inspired multi-objective optimization algorithms.

Many problems from science and industry have several (and normally conflicting) objectives that have to be optimized at the same time. Such problems are called multi-objective optimization problems and have been subject of research in the past two decades. One of the reasons why evolutionary algorithms are so suitable for multi-objective optimization is because they can generate a whole set of solutions (the Pareto-optimal solutions) in a single run rather than requiring an iterative one-solution-at-a-time process as followed in traditional mathematical programming techniques.

The main aim of this special session organized within the 2014 IEEE Congress on Evolutionary Computation (CEC'2014) is to bring together both experts and new-comers working on Evolutionary Multi-objective Optimization (EMO) to discuss new and exciting issues in this area.

We encourage submission of papers describing new concepts and strategies, and systems and tools providing practical implementations, including hardware and software aspects. In addition, we are interested in application papers discussing the power and applicability of these novel methods to real-world problems in different areas in science and industry. You are invited to submit papers that are unpublished original work for this special session at CEC 2014. The topics are, but not limited to, the following

1. Many-objective optimization
2. Theoretical aspects of EMO algorithms 
3. Real-world applications of EMO algorithms
4. Test and benchmark problems for EMO algorithms 
5. New EMO techniques including those using meta-heuristics such as artificial immune systems, particle swarm optimization, differential evolution, cultural algorithms, etc. 
6. Multi-objectivization and visualization techniques
7. Handling practicalities, such as uncertainty, noise, constraints, dynamically changing problems, bi-level problems, mixed-integer problems, computationally expensive problems, fixed budget of evaluations, etc.
8. Performance measures for EMO algorithms
9. Techniques to keep diversity in the population 
10. Comparative studies of EMO algorithms 
11. Memetic and Metaheuristics based EMO algorithms 
12. Hybrid approaches combining, for example, EMO algorithms with mathematical programming techniques and exact methods
13. Parallel EMO approaches 
14. Adaptation, learning, and anticipation
15. Evolutionary multi-objective combinatorial optimization, EMO control problems, EMO inverse problems, EMO data mining, EMO machine learning

More Information about EMO including papers, dissertations, test problems and general up-to-date information can be found on the EMOO repository webpage: 
http://delta.cs.cinvestav.mx/~ccoello/EMOO/

Author's Schedule

For the deadline for submitting papers, please check the website of WCCI 2014:
http://www.ieee-wcci2014.org/

IEEE Transactions on Fuzzy Systems: Volume 21, Issue 5, October 2013

1. Fuzzy-Model-Based Fault-Tolerant Design for Nonlinear Stochastic Systems Against Simultaneous Sensor and Actuator Faults
Author(s): Ming Liu ; Xibin Cao ; Peng Shi
Page(s): 789-799

2. Stability Analysis of Polynomial-Fuzzy-Model-Based Control Systems Using Switching Polynomial Lyapunov Function
Author(s): Lam, H.K. ; Narimani, M. ; Hongyi Li ; Honghai Liu
Page(s): 800-813

3. Hierarchical Clustering Problems and Analysis of Fuzzy Proximity Relation on Granular Space
Author(s): Xu-Qing Tang ; Ping Zhu
Page(s): 814-824

4. RFRR: Robust Fuzzy Rough Reduction
Author(s): Suyun Zhao ; Hong Chen ; Cuiping Li ; Mengyao Zhai ; Xiaoyong Du
Page(s): 825-841

5. Model Checking of Linear-Time Properties Based on Possibility Measure
Author(s): Yongming Li ; Lijun Li
Page(s): 842-854

6. Clustering Spatiotemporal Data: An Augmented Fuzzy C-Means
Author(s): Izakian, H. ; Pedrycz, W. ; Jamal, I.
Page(s): 855-868

7. Conditional Density Estimation Using Probabilistic Fuzzy Systems
Author(s): van den Berg, J. ; Kaymak, U. ; Almeida, R.J.
Page(s): 869-882

8. Robust Stability and Stabilization of Uncertain T–S Fuzzy Systems With Time-Varying Delay: An Input–Output Approach
Author(s): Lin Zhao ; Huijun Gao ; Karimi, H.R.
Page(s): 883-897

9. Multiary α-Resolution Principle for a Lattice-Valued Logic
Author(s): Yang Xu ; Jun Liu ; Xiaomei Zhong ; Shuwei Chen
Page(s): 898-912

10. Adaptive Fuzzy Decentralized Output Feedback Control for Nonlinear Large-Scale Systems With Unknown Dead-Zone Inputs
Author(s): Shaocheng Tong ; Yongming Li
Page(s): 913-925

11. Chaos-Based Fuzzy Regression Approach to Modeling Customer Satisfaction for Product Design
Author(s): Huimin Jiang ; Kwong, C.K. ; Ip, W.H. ; Zengqiang Chen
Page(s): 926-936

12. Induction of Shadowed Sets Based on the Gradual Grade of Fuzziness
Author(s): Tahayori, H. ; Sadeghian, A. ; Pedrycz, W.
Page(s): 937-949

13. A Genetic Fuzzy Linguistic Combination Method for Fuzzy Rule-Based Multiclassifiers
Author(s): Trawinski, K. ; Cordon, O. ; Sanchez, L. ; Quirin, A.
Page(s): 950-965

14. Network-Based Robust Passive Control for Fuzzy Systems With Randomly Occurring Uncertainties
Author(s): Zheng-Guang Wu ; Peng Shi ; Hongye Su ; Jian Chu
Page(s): 966-970

15. A Simple Fuzzy Method to Remove Mixed Gaussian-Impulsive Noise From Color Images
Author(s): Camarena, J.-G. ; Gregori, V. ; Morillas, S. ; Sapena, A.
Page(s): 971-977

16. Proximity-Based Clustering: A Search for Structural Consistency in Data With Semantic Blocks of Features
Author(s): Pedrycz, W.
Page(s): 978-982

17. A Note on Fuzzy Relational Equations With Min-Implication Composition
Author(s): Pingke Li
Page(s): 983-986

18. Comments on “Quantized Control Design for Impulsive Fuzzy Networked Systems”
Author(s): Guotao Hui ; Jun Yang ; Bonan Huang
Page(s): 987