2008 International Conference on Fuzzy Systems

Hong Kong, June 1-6, 2008, FUZZ’2008

Special Session Fuzzy Systems: Control and Hardware (FS10)

Organized by: Prof. Oscar Castillo and Prof. Patricia Melin

 ocastillo@hafsamx.org      epmelin@hafsamx.org

 

Background

Fuzzy logic is an area of soft computing that enables a computer system to reason with uncertainty. A fuzzy inference system consists of a set of if-then rules defined over fuzzy sets. Fuzzy sets generalize the concept of a traditional set by allowing the membership degree to be any value between 0 and 1. This corresponds, in the real world, to many situations where it is difficult to decide in an unambiguous manner if something belongs or not to a specific class. Fuzzy expert systems, for example, have been applied with some success to problems of decision, control, diagnosis and classification, just because they can manage the complex expert reasoning involved in these areas of application. The main disadvantage of fuzzy systems is that they can't adapt to changing situations. For this reason, it is a good idea to combine fuzzy logic with neural networks or genetic algorithms, because either one of these last two methodologies could give adaptability to the fuzzy system. On the other hand, the knowledge that is used to build these fuzzy rules is uncertain. Such uncertainty leads to rules whose antecedents or consequents are uncertain, which translates into uncertain antecedent or consequent membership functions. Type-1 fuzzy systems, like the ones mentioned above, whose membership functions are type-1 fuzzy sets, are unable to directly handle such uncertainties. In this case, type-2 fuzzy systems, in which the antecedent or consequent membership functions are type-2 fuzzy sets are better suited for modeling these problems. Such sets are fuzzy sets whose membership grades themselves are type-1 fuzzy sets; they are very useful in circumstances where it is difficult to determine an exact membership function for a fuzzy set. Another way to handle this higher degree of uncertainty is to use intuitionistic fuzzy logic, which can also be considered as a generalization of type-1 fuzzy logic. In intuitionistic fuzzy logic the uncertainty in describing fuzzy sets is modeled by using at the same time the membership function and the non-membership function of a set.

In real-world applications there is need for hardware implementations of fuzzy systems because efficiency and robustness are critical. Also, real-time solutions are required, and this can be achieved by having hardware implementations of the fuzzy systems. As an example, in most of the applications of fuzzy controllers it is more appropriate to have a hardware implementation of the fuzzy systems. Most of the previous work on fuzzy hardware has been done for type-1 fuzzy systems, but there is also a need of investigating how to implement non-standard fuzzy systems (like type-2 or others). Also, there is a need to investigate hardware implementations of neuro-fuzzy and genetic-fuzzy systems, which are very important for achieving adaptation in intelligent systems. For these reasons, papers that are related to these themes are welcomed in this Special Session.

 

Motivation for the Special Session

Most of the previous work on fuzzy hardware has been done for type-1 fuzzy systems, but there is need of investigating how to implement non-standard fuzzy systems (like type-2 or others). Also, there is a need to investigate hardware implementations of neuro-fuzzy and genetic-fuzzy systems, which are very important for achieving adaptation in intelligent systems. Detailed methods for implementing different types of fuzzy systems in solving real-world problems will also be considered. Hardware implementations of fuzzy systems with applications on the following areas will be considered: Robotic and Automation, Control of Non-linear Plants, Manufacturing Systems, and Pattern Recognition.

 

 

BRIEF CVs

 

Oscar Castillo holds the Doctor in Science degree (Doctor Habilitatus) in Computer Science from the Polish Academy of Sciences (with the Dissertation “Soft Computing and Fractal Theory for Intelligent Manufacturing”). He is a Professor of Computer Science in the Graduate Division, Tijuana Institute of Technology, Tijuana, Mexico. In addition, he is serving as Research Director of Computer Science and head of the research group on fuzzy logic and genetic algorithms. Currently, he is President of HAFSA (Hispanic American Fuzzy Systems Association) and Vice-President of IFSA (International Fuzzy Systems Association) in charge of publicity. Prof. Castillo is also Vice-Chair of the Mexican Chapter of the Computational Intelligence Society (IEEE). Prof. Castillo is also General Chair of the IFSA 2007 World Congress to be held in Cancun, Mexico. He also belongs to the Technical Committee on Fuzzy Systems of IEEE and to the Task Force on “Extensions to Type-1 Fuzzy Systems”. He is also a member of NAFIPS, IFSA and IEEE. He belongs to the Mexican Research System. His research interests are in Type-2 Fuzzy Logic, Intuitionistic Fuzzy Logic, Fuzzy Control, Neuro-Fuzzy and Genetic-Fuzzy hybrid approaches. He has published over 60 journal papers, 5 authored books, 10 edited books, and 160 papers in conference proceedings. He has been Guest Editor of several successful Special Issues in the past, like “Soft Computing for Control of Non-Linear Dynamical Systems” in the Journal of Applied Soft Computing, 2003 (Elsevier), “Hybrid Intelligent Systems” in the Journal of Non-Linear Studies, 2004 (I&S Publishers), and “Soft Computing for Modeling, Simulation, and “Control of Non-Linear Dynamical Systems” in the Journal of Intelligent Systems, 2005 (Wiley). Web Page: www.hafsamx.org/castillo

Patricia Melin. She holds the Doctor in Science degree (Doctor Habilitatus D.Sc.) in Computer Science from the Polish Academy of Sciences (with the Dissertation “Hybrid Intelligent Systems for Pattern Recognition using Soft Computing”). She is a Professor of Computer Science in the Graduate Division, Tijuana Institute of Technology, Tijuana, Mexico, since 1998. In addition, she is serving as Director of Graduate Studies in Computer Science and head of the research group on Computational Intelligence (2000-present). Currently, she is Vice President of HAFSA (Hispanic American Fuzzy Systems Association) and Program Chair of International Conference FNG’05, FNG’07. She is also Program Chair of the IFSA 2007 World Congress to held in Cancun, Mexico. Prof. Melin is the founding Chair of the Mexican Chapter of the IEEE Computational Intelligence Society. She is Vice Chair of the IEEE Neural Network Technical Committee (2007), she also belongs to the IEEE CIS Educational Committee, the Committee of Women in Computational Intelligence of the IEEE and to the New York Academy of Sciences. She is member of NAFIPS, IFSA, and senior member of IEEE. She belongs to the Mexican Research System with level II. Her research interests are in Type-2 Fuzzy Logic, Modular Neural Networks, Pattern Recognition, Fuzzy Control, Neuro-Fuzzy and Genetic-Fuzzy hybrid approaches. She has published over 50 journal papers, 5 authored books, 6 edited books, and 160 papers in conference proceedings. She has served as Guest Editor of several Special Issues in the past, like “Soft Computing for Control of Non-Linear Dynamical Systems” in the Journal of Applied Soft Computing, 2003 (Elsevier), “Hybrid Intelligent Systems” in the Journal of Non-Linear Studies, 2004 (I&S Publishers), and “Soft Computing for Modeling, Simulation, and “Control of Non-Linear Dynamical Systems” in the Journal of Intelligent Systems, 2005 (Wiley), “Hybrid Intelligent Systems” in the Journal of Information Sciences, 2007 (Elsevier).

Web page: www.hafsamx.org/melin