Fuzzy Logic: Intelligence, Control, and Information

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9780135258170: Fuzzy Logic: Intelligence, Control, and Information
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Highlights motivations and benefits of employing fuzzy logic in control engineering and information systems. Providing equal emphasis on theoretical foundations and practical issues, this book features fuzzy logic concepts and techniques in intelligent systems, control, and information technology. Uses Fuzzy Logic Toolbox for MATLAB™ to demonstrate exemplar applications and to develop hands-on exercises. Introduces the connection between fuzzy logic and related ideas, methods, and theories developed in other disciplines (e.g., artificial intelligence, probability theory, control, model identification, etc.).

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From the Back Cover:

Providing equal emphasis on theoretical foundations and practical issues, this book features fuzzy logic concepts and techniques in intelligent systems, control, and information technology.

FEATURES

  • Comprehensive and up-to-date coverage.
  • Provides examples and exercises that are related to real world problems.
  • Uses Fuzzy Logic Toolbox for MATLAB™ to demonstrate exemplar applications and to develop hands-on exercises.
  • Provides design guidelines and design methods for developing fuzzy logic applications.
  • Provides a modern perspective of the fuzzy logic technology.
  • Introduces core concepts and techniques gently in two steps.
  • Provides relevant background materials that encompass a wide range of disciplines.
  • Introduces the connection between fuzzy logic and related ideas, methods, and theories developed in other disciplines (e.g., artificial intelligence, probability theory, control, model identification, etc.).
  • Summarizes key concepts at the end of each chapter.
  • Highlights motivations and benefits of employing fuzzy logic in control engineering and information systems.
  • Discussion of open research issues and their implications.
  • A chapter is devoted to the subject of "How to design a fuzzy system." In addition, several other chapters (e.g., the chapters on fuzzy model identification and neuro-fuzzy systems) introduce techniques for automating decisions in the design process using training data (i.e., examples) and learning algorithms.
  • Advanced topics are separated from the basic material in the chapter that immediately follows.

About the Author:

Professors John Yen and Reza Langari are on the faculty of Texas A&M University and co-founder of the Center for Fuzzy Logic, Robotics, and Intelligent Systems. They are the co-editors of the IEEE Press Book ``Industrial Applications of Fuzzy Logic and Intelligent Systems''. They are also the General Chair and the Program Chair of The 9th IEEE International Conference on Fuzzy Systems (FUZZIEEE 2000).

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