Please download the program schedule (PDF)

Keynote Speaker:

Prof. Gang Feng has been with City University of Hong Kong since 2000, where he is currently a professor, and was with School of Electrical Engineering, University of New South Wales, Australia, 1992-1999. He was awarded the IEEE Transactions on Fuzzy Systems Outstanding Paper Award in 2007 and an Alexander von Humboldt Fellowship in 1997-1998. He was a visiting Fellow at National University of Singapore (1997), and Aachen Technology University, Germany (1997-1998). He has author or co-authored over 150 international journal papers including over 60 in IEEE Transactions. His current research interests include hybrid systems and control, system biology, and intelligent systems and control.
Prof. Feng is an associate editor of IEEE Trans. on Automatic Control, IEEE Trans. on Fuzzy Systems, and Journal of Control Theory and Applications, and was an associate editor of IEEE Trans. on Systems, Man & Cybernetics, Part C and the Conference Editorial Board of IEEE Control System Society.

Title: Model Based Fuzzy Logic Control: Overview and Perspectives

Abstract: Fuzzy logic control was originally introduced and developed as a model free control design approach. However it unfortunately suffers from criticism of lacking of systematic stability analysis and controller design though it has a great success in industry applications. In the past ten years or so, prevailing research effort on fuzzy logic control have been devoted to model based fuzzy control systems that guarantee not only stability but also performance of closed loop fuzzy control systems. This talk presents a survey on recent developments of analysis and design of model based fuzzy control systems. Attention will be focused on stability analysis and controller design based on the so-called Takagi-Sugeno fuzzy models or fuzzy dynamic models. Perspectives of model based fuzzy control in future are also discussed.

Prof. Van-Nam Huynh holds a Bachelor degree in Mathematics (1990) and a PhD (1999) from the University of Quinhon and the Institute of Information Technology, Vietnamese Academy of Science and Technology, Vietnam, respectively.  He was a post-doctoral fellow (2001-2002) awarded by Inoue Foundation for Science at Japan Advanced Institute of Science and Technology (JAIST), where currently he holds an assistant professor position.  His research interests include decision theories, computing and reasoning with words, information fusion, kansei information processing and application, and machine learning. He has published over 60 refereed papers on these subjects and has served on the program committees of a number of international conferences and workshops. He has organized and been program chair of the International Workshop on Interval/Probabilistic Uncertainty and Non-classical Logics held at JAIST in March 2008.

Title: Target-Oriented Evaluation and Decision Models with Applications

Abstract: Thinking of targets is quite natural in many decision-making situations. During the last decade, target-oriented decision models have been extensively discussed and studied in the literature, for decision-making under uncertainty with a single evaluation attribute as well as for multi-attribute decision-making problems. For the former, it has been shown that the target-oriented decision model satisfies the Savage axioms serving as an axiomatic foundation for rational decision making, while for the latter, equivalent target-oriented formulations for some classes of multi-attribute utility functions have been also established.  This talk will start with a brief introduction to the target-oriented decision analysis, after which we will focus on the fuzzy target-based decision model for decision-making under uncertainty, along with showing an interesting link between the decision maker's different attitudes about target and different risk attitudes in terms of utility functions. We will then discuss current research on a fuzzy target-oriented approach to multi-attribute evaluation problems with a practical application in consumer-oriented evaluation of Japanese traditional crafts using kansei data. Finally, we will conclude the talk by highlighting some directions for future research as well as some potential applications in screening evaluations for product innovation and recommendation systems.


Prof. Peter Kokol has been with laboratory for system design, faculty of Electrical Engineering and Computer Science, University of Maribor, Slovenia where he is currently a professor, and was with professor of Computer Science at Faculty of Electrical Engineering and Computer Science, Maribor, professor of Computer Science at Medical Faculty, Maribor, head of Laboratory for System Design at Faculty of Electrical Engineering and Computer Science, Maribor, associate dean for Research at University College of Nursing (Faculty for Health Sciences), Maribor, director for Center of Multidisciplinary Research and Studies (CIMRS)  and was with associations of Chair of Technical Committee on Computational Medicine, IEEE, Member of ACM, ASIS, SDMI.

Title: Improving medical decision making by self organizing intelligent systems

Abstract: Early and accurate diagnosing of various diseases has proved to be of vital importance in many health care processes. In recent years intelligent systems have been often used for decision support and classification in many scientific and engineering disciplines including health care. However, in many cases the proposed treatment, prediction or diagnose can differ from one intelligent system to another, similar to the real world where different medical specialists may have different opinions. Indeed, in real world specialists’ opinions complement one another and when integrated they usually form a better solution. Our novel idea presented in this paper is to mimic this real world situation in the manner to merge different opinions generated by different intelligent systems using the self organizing abilities of cellular automata. We empirically show that classification cellular automata combining many various methods for classifier construction eventually outperforms single classifiers and conventional integration methods in terms of accuracy and class accuracy.