Overview

"This exceptional guide and reference is devised for practitioners who want to employ fuzzy logic concepts in the design and deployment of actual fuzzy systems. FUZZY SYSTEMS DESIGN PRINCIPLES concentrates on the IF-THEN fuzzy algorithm, one of the most popular algorithms implemented today. The ""basic fuzzy inference algorithm,"" the IF-THEN structure is not only applicable to many types of problems, but is also comprised of building blocks used in the development of other types of fuzzy systems used in today's electronic and software products. Sponsored by: IEEE Neural Networks Council."

Full Product Details

Author:   Riza C. Berkan ,  Sheldon L. Trubatch
Publisher:   John Wiley and Sons Ltd
Imprint:   John Wiley & Sons Inc
Dimensions:   Width: 15.90cm , Height: 3.30cm , Length: 23.10cm
Weight:   0.888kg
ISBN:  

9780780311510

ISBN 10:   0780311515
Pages:   514
Publication Date:   11 May 1997
Audience:   Professional and scholarly ,  General/trade ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Out of Stock Indefinitely
Availability:   Out of stock  

Table of Contents

Foreword. Preface. Chapter 1: Introduction. 1.1 Partial Truth and Fuzziness. 1.2 Foundation of Fuzzy Systems. 1.3 Fuzzy Systems at Work. 1.4 Fuzzy System Design. 1.5 How to Use This Book Effectively. 1.6 Terminology and Conventions. References. Chapter 2: Theory. 2.1 Crisp Versus Fuzzy Sets. 2.2 From Fuzzy Sets to Fuzzy Events. 2.3 Fuzzy Logic and Linguistics. 2.4 Practical Fuzzy Measures. 2.5 Fuzzy Set Operations. 2.6 Properties of Fuzzy Sets. 2.7 Fuzzification Techniques. 2.8 Alpha Cuts. 2.9 Relational Inference. 2.10 Compositional Inference. 2.11 Linguistic Variables and Logic Operators. 2.12 Inference Using Fuzzy Variables. 2.13 Fuzzy Implication. 2.14 Fuzzy Systems and Algorithms. 2.15 Defuzzification. 2.16 Adaptive Fuzzy Systems and Algorithms. 2.17 Expert Systems Versus Fuzzy Inference Engines. References. Chapter 3: The Basic Fuzzy Inference Algorithm. 3.1 Introduction. 3.2 Overall Algorithm. 3.3 Input Data Processing. 3.4 Evaluating Antecedent Fuzzy Variables. 3.5 Left-Hand-Side Computations. 3.6 Right-Hand-Side Computations. 3.7 Output Processing. Problems. References. Chapter 4: Conceptual Design. 4.1 Introduction. 4.2 Fuzzy System Design and Its Elements. 4.3 Design Options, Processes, and Background. 4.4 Requirements. 4.5 Knowledge Acquisition. 4.6 The First Principle of Fuzzy Inference Design. 4.7 Linguistic Design Criteria. 4.8 Application of the Design Criteria. 4.9 Systems Ontology and Problem Types. 4.10 Useful Tools Supporting Design. References. Recommended Books for Design. Chapter 5: Fuzzy Variable Design. 5.1 Introduction to Fuzzy Variable Design. 5.2 Data-Driven Fuzzy Variable Design. 5.3 Linguistic Fuzzy Variable Design. 5.4 Practical Design Considerations. 5.5 Summary. Problems. References. Chapter 6: Membership Function Shape Analysis. 6.1 Introduction to Shape Analysis. 6.2 Membership Function Height. 6.3 Membership Function Line Style. 6.4 Overlapping. 6.5 Summary. Problems. Chapter 7: Composing Fuzzy Rules. 7.1 Introduction. 7.2 Basic Logic Operators. 7.3 Logic Operator Design Issues. 7.4 Rule Formation Per Inference Type. 7.5 Rule Composition Strategies. 7.6 Paradoxical Cases. 7.7 Membership Function Shape Effects. 7.8 Summary. References. Selected Bibliography. Chapter 8: Implication Design. 8.1 Introduction. 8.2 Selecting Implication Operators. 8.3 Behavioral Properties. 8.4 Aggregation Design. 8.5 Designing a Defuzzification/Decomposition Process. 8.6 Interpreting Output Fuzzy Sets. 8.7 Summary. Appendix: The Basic Fuzzy Inference Algorithm. Index.

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Author Information

Dr. Riza C. Berkan is the president of Monitoring Diagnostics and Control (MODiCO), Inc., and is a part-time faculty member at the University of Tennessee where he teaches a graduate-level fuzzy logic course offered by the Department of Engineering Science and Mechanics. Dr. Berkan is the author of several articles on fuzzy logic and its applications to the operations of large-scale, complex systems. Dr. Sheldon L. Trubatch is currently a partner at the law firm of Winston & Strawn, where he specializes in nuclear and administrative law. He is also developing applications of fuzzy logic to support legal and regulatory decision making. Previously, Dr. Trubatch was a physics professor at California State University, Long Beach. Among his research areas was the application of mathematical techniques to the description of biological systems. Dr. Trubatch has a J.D. from Columbia University School of Law and a Ph.D. in physics from Brandeis University.

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