Integration of Fuzzy Logic and Chaos Theory

Hardcover  VIII, 625 p. 345 illus.,  ISBN 3540268995      £69.00

This book attempts to present some current research progress and results on the interplay of fuzzy logic and chaos theory. More specifically, this book includes a collections of some state-of-the-art surveys, tutorials, and application examples written by some experts working in the interdisciplinary fields overlapping fuzzy logic and chaos theory. The content of the book covers fuzzy definition of chaos, fuzzy modeling and control of chaotic systems using both Mamdani and Takagi-Sugeno models, fuzzy model identification using genetic algorithms and neural network schemes, bifurcation phenomena and self-referencing in fuzzy systems, complex fuzzy systems and their collective behaviours, as well as some applications of combining fuzzy logic and chaotic dynamics, such as fuzzy-chaos hybrid controllers for nonlinear dynamic systems, and fuzzy-model-based chaotic cryptosystems. This book can serve as a handy reference for researchers working in the interdisciplines related, among others, to both fuzzy logic and chaos theory.

Written for: Researchers, engineers, graduate students in Soft computing, Fuzziness and Complexity/Nonlinear Systems

Contents

• Beyond the Li-Yorke definition of chaos.
• Chaotic dynamics with fuzzy systems
• Fuzzy modeling and control of chaotic systems.
• Fuzzy model identification using a hybrid mGA scheme with application to chaotic system modeling.
• Fuzzy control of chaos.
• Chaos control using fuzzy controllers.
• Digital fuzzy set-point regulated chaotic systems: An intelligent digital redesign approach.
• Anti-control of chaos for Takagi-Sugeno fuzzy systems.
• Fuzzy chaos synchronization via sampled driving signals.
• Bifurcation phenomena in elementary Takagi-Sugeno fuzzy systems.
• Self-reference, chaos, and fuzzy logic.
• Chaotic behaviour in recurrent Takagi-Sugeno models.
• A theory of fuzzy chaos for simulation and control of nonlinear systems.
• Complex fuzzy systems and their collective behaviour.
• Real-time identification and forecasting of chaotic time series using hybrid systems of computational intelligence
• Fuzzy-chaos hybrid controllers for nonlinear dynamic systems
• Fuzzy model-based chaotic cryptosystems.
• Evolution of complexity.
• Problem solving via fuzziness-based coding of continuous constraints yielding synergetic and chaos-dependent origination structures.
• Some applications of fuzzy dynamic models with chaotic properties.