To Be Announced
Ronald Yager
Machine Intelligence Institute, Iona College, USA

 [Abstract] TBA

 [Biography] Ronald R. Yager has worked in the area of machine intelligence for over twenty-five years. He has published over 500 papers and fifteen books. He was the recipient of the IEEE Computational Intelligence Society Pioneer award in Fuzzy Systems. Dr. Yager is a fellow of the IEEE, the New York Academy of Sciences and the Fuzzy Systems Association. He was given a lifetime achievement award by the Polish Academy of Sciences for his contributions. He served at the National Science Foundation as program director in the Information Sciences program. He was a NASA/Stanford visiting fellow and a research associate at the University of California, Berkeley. He has been a lecturer at NATO Advanced Study Institutes. He has been a distinguished honorary professor at the Aalborg University Esbjerg Denmark. He is an affiliated distinguished researcher at the European Centre for Soft Computing. He received his undergraduate degree from the City College of New York and his Ph. D. from the Polytechnic University of New York. Currently, he is Director of the Machine Intelligence Institute and Professor of Information Systems at Iona College. He is editor and chief of the International Journal of Intelligent Systems. He serves on the editorial board of numerous technology journals including the IEEE Transactions on Fuzzy Systems, Neural Networks, Data Mining and Knowledge Discovery, IEEE Intelligent Systems, Fuzzy Sets and Systems, the Journal of Approximate Reasoning and the Journal of Group Decision Making and Negotiations. He has made fundamental contributions in decision making under uncertainty and the fusion of information. Much of his work has been transitioned into commercial applications.

Design of Hybrid Intelligent Systems with Modular Neural Networks and Type-2 Fuzzy Systems for Pattern Recognition
Patricia Melin
Division of Graduate Studies, Tijuana Institute of Technology, Tijuana, Mexico

 [Abstract] This talk describes hybrid intelligent systems using type-2 fuzzy logic and modular neural networks for pattern recognition applications. Hybrid intelligent systems combine several intelligent computing paradigms, including fuzzy logic, neural networks, and bio-inspired optimization algorithms, which can be used to produce powerful pattern recognition systems. Type-2 fuzzy logic is an extension of traditional type-1 fuzzy logic that enables managing higher levels of uncertainty in complex real world problems, which are of particular importance in the area of pattern recognition. The first part of the talk describes theory and design algorithms, which are the basis for achieving intelligent pattern recognition. The second part of the talk describes type-2 fuzzy models and modular neural networks with the aim of designing intelligent systems for complex pattern recognition problems, including iris, ear, face and voice recognition. The third part of the talk describes evolutionary optimization of type-2 fuzzy systems and modular neural networks in the area of intelligent pattern recognition, which includes the application of genetic and bio-inspired algorithms for obtaining optimal type-2 fuzzy integration systems and ideal neural network architectures for solving problems in this area.

 [Biography] Patricia Melin received the D.S. degree (habilitatus) in computer science from the Polish Academy of Sciences, Warsaw, Poland. She has been a Professor of computer science in the Graduate Division, Tijuana Institute of Technology, Tijuana, Mexico, since 1998. She is currently the Director of the graduate studies in computer science and the Head of the Research Group of Computational Intelligence, since 2000. She has published over 300 papers, 8 authored books, and 12 edited books. Her current research interests are in modular neural networks, pattern recognition, type-2 neuro-fuzzy, and neuro-genetic fuzzy hybrid approaches. She is currently the Chair of the Task Force on Hybrid Intelligent Systems of Neural Networks Technical Committee of the IEEE Computational Intelligence Society and is the Founding Chair of the Mexican Chapter of the IEEE Computational Intelligence Society. Currently, she is the President of HAFSA (Hispanic American Fuzzy Systems Association) and a Member of the IEEE Neural Network Technical Committee. She has served as a Guest Editor of several Special Issues in the past, in journals: Applied Soft Computing, Intelligent Systems, Information Sciences, Non-Linear Studies, JAMRIS, Fuzzy Sets and Systems, and Engineering Letters. She is associate editor of several journals, such as the IEEE Transactions on Neural Networks and Learning Systems, and belongs to the Editorial Board of several important journals, like the Journal of Advanced Robotic Systems.

Bio-Inspired Optimization of Type-2 Fuzzy Systems in Intelligent Control Applications
Oscar Castillo
Division of Graduate Studies, Tijuana Institute of Technology, Tijuana, Mexico

 [Abstract] Hybrid intelligent systems based on type-2 fuzzy logic for achieving intelligent control are of crucial importance in practice to manage the high degrees of uncertainty present in real world processes. Hybrid intelligent systems usually combine several intelligent computing paradigms, including fuzzy logic, and bio-inspired optimization algorithms, which can be used to produce powerful automatic control systems. This talk will cover evolutionary optimization of type-2 fuzzy systems in intelligent control with the aim of designing optimal type-2 fuzzy controllers for complex control problems in diverse areas of application, including mobile robotics, aircraft dynamic systems and hardware implementations. This talk will also deal with the theme of bio-inspired optimization of type-2 fuzzy systems in intelligent control, which includes the application of particle swarm intelligence and ant colony optimization algorithms for obtaining optimal type-2 fuzzy controllers.

 [Biography] 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 Vice-President of HAFSA (Hispanic American Fuzzy Systems Association) and President of IFSA (International Fuzzy Systems Association). Prof. Castillo is also Chair of the Mexican Chapter of the Computational Intelligence Society (IEEE). 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. 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 340 papers, including 100 journal papers and 200 papers in conference proceedings, and has published 7 authored books and 20 edited books,. He is Associate Editor of several journals, like the IEEE Transactions on Fuzzy Systems, and the Journal of Information Sciences. 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