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Fuzzy Chaotic Systems

Modeling, Control, and Applications

  • Book
  • © 2006

Overview

Authors:
  1. Zhong Li

    1. Faculty of Electrical and Computer Engineering, FernUniversitaet in Hagen, Hagen, Germany

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  • Overviews the theory, methodologies and applications of the fascinating field of Fuzzy systems and Chaos
  • Generates a great potential to attract more attention to combine fuzzy systems with
  • Chaos theory and contains important seeds for future scientific research and engineering applications

Part of the book series: Studies in Fuzziness and Soft Computing (STUDFUZZ, volume 199)

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Table of contents (14 chapters)

  1. Front Matter

    Pages I-XII
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  2. Introduction

    • Zhong Li
    Pages 1-11

  3. Fuzzy Logic and Fuzzy Control

    • Zhong Li
    Pages 13-29

  4. Chaos and Chaos Control

    • Zhong Li
    Pages 31-52

  5. Definition of Chaos in Metric Spaces of Fuzzy Sets

    • Zhong Li
    Pages 53-72

  6. Fuzzy Modeling of Chaotic Systems – I (Mamdani Model)

    • Zhong Li
    Pages 73-89

  7. Fuzzy Modeling of Chaotic Systems – II (TS Model)

    • Zhong Li
    Pages 91-119

  8. Fuzzy Control of Chaotic Systems – I (Mamdani Model)

    • Zhong Li
    Pages 121-141

  9. Adaptive Fuzzy Control of Chaotic Systems (Mamdani Model)

    • Zhong Li
    Pages 143-151

  10. Fuzzy Control of Chaotic Systems – II (TS Model)

    • Zhong Li
    Pages 153-187

  11. Synchronization of TS Fuzzy Systems

    • Zhong Li
    Pages 189-203

  12. Chaotifying TS Fuzzy Systems

    • Zhong Li
    Pages 205-238

  13. Intelligent Digital Redesign for TS Fuzzy Systems

    • Zhong Li
    Pages 239-253

  14. Spatiotemporal Chaos and Synchronization in Complex Fuzzy Systems

    • Zhong Li
    Pages 255-273

  15. Fuzzy-chaos-based Cryptography

    • Zhong Li
    Pages 275-283

  16. Back Matter

    Pages 285-299
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Keywords

About this book

Bringing together the two seemingly unrelated concepts,fuzzy logic andchaos theory,isprimarilymotivatedbytheconceptofsoft computing (SC),initiated by Lot? A. Zadeh, the founder of fuzzy set theory. The principal constituents of SC are fuzzy logic (FL), neural network theory (NN) and probabilistic reasoning (PR), with the latter subsuming parts of belief networks, genetic algorithms, chaos theory and learning theory. What is important to note is that SC is not a melange of FL, NN and PR. Rather, it is an integration in which each of the partners contributes a distinct methodology for addressing problems in their common domain. In this perspective, the principal cont- butions of FL, NN and PR are complementary rather than competitive. SC di?ers from conventional (hard) computing in that it is tolerant of imprecision, uncertainty and partial truth. In e?ect, the role model for soft computing is the human mind. From the general SC concept, we extract FL and chaos theory as the object of this book to study their relationships or interactions. Over the past few decades, fuzzy systems technology and chaos theory have received ever increasing research interests from, respectively, systems and control engineers, theoretical and experimental physicists, applied ma- ematicians, physiologists, and other communities of researchers. Especially, as one of the emerging information processing technologies, fuzzy systems technology has achieved widespread applications around the globe in many industriesandtechnical?elds,rangingfromcontrol,automation,andarti?cial intelligence (AI) to image/signal processing and pattern recognition. On the otherhand,inengineeringsystemschaostheoryhasevolvedfrombeingsimply a curious phenomenon to one with real, practical signi?cance andutilization.

Authors and Affiliations

  • Faculty of Electrical and Computer Engineering, FernUniversitaet in Hagen, Hagen, Germany

    Zhong Li

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