Genetic fuzzy systems : evolutionary tuning and learning of fuzzy knowledge bases / Oscar Cordon ... [et al.].

Format:

Book

Contributor:

Cordón, Oscar.

Series:

Advances in fuzzy systems ; v. 19.

Advances in fuzzy systems ; v. 19

Language:

English

Subjects (All):

Fuzzy systems.

Genetics--Mathematical models.

Genetics.

Physical Description:

1 online resource (489 p.)

Edition:

1st ed.

Place of Publication:

Singapore ; River Edge, NJ : World Scientific, c2001.

Language Note:

English

Summary:

In recent years, a great number of publications have explored the use of genetic algorithms as a tool for designing fuzzy systems. Genetic Fuzzy Systems explores and discusses this symbiosis of evolutionary computation and fuzzy logic. The book summarizes and analyzes the novel field of genetic fuzzy systems, paying special attention to genetic algorithms that adapt and learn the knowledge base of a fuzzy-rule-based system. It introduces the general concepts, foundations and design principles of genetic fuzzy systems and covers the topic of genetic tuning of fuzzy systems. It also introduces t

Contents:

Foreword; Preface; Contents; Chapter 1 Fuzzy Rule-Based Systems; 1.1 Framework: Fuzzy Logic and Fuzzy Systems; 1.2 Mamdani Fuzzy Rule-Based Systems; 1.3 Takagi-Sugeno-Kang Fuzzy Rule-Based Systems; 1.4 Generation of the Fuzzy Rule Set; 1.5 Applying Fuzzy Rule-Based Systems; Chapter 2 Evolutionary Computation; 2.1 Conceptual Foundations of Evolutionary Computation; 2.2 Genetic Algorithms; 2.3 Other Evolutionary Algorithms; Chapter 3 Introduction to Genetic Fuzzy Systems; 3.1 Soft Computing; 3.2 Hybridisation in Soft Computing; 3.3 Integration of Evolutionary Algorithms and Fuzzy Logic

3.4 Genetic Fuzzy SystemsChapter 4 Genetic Tuning Processes; 4.1 Tuning of Fuzzy Rule-Based Systems; 4.2 Genetic Tuning of Scaling Functions; 4.3 Genetic Tuning of Membership Functions of Mamdani Fuzzy Rule-Based Systems; 4.4 Genetic Tuning of TSK Fuzzy Rule Sets; Chapter 5 Learning with Genetic Algorithms; 5.1 Genetic Learning Processes. Introduction; 5.2 The Michigan Approach. Classifier Systems; 5.3 The Pittsburgh Approach; 5.4 The Iterative Rule Learning Approach; Chapter 6 Genetic Fuzzy Rule-Based Systems Based on the Michigan Approach; 6.1 Basic Features of Fuzzy Classifier Systems

6.2 Fuzzy Classifier Systems for Learning Rule Bases6.3 Fuzzy Classifier Systems for Learning Fuzzy Rule Bases; Chapter 7 Genetic Fuzzy Rule-Based Systems Based on the Pittsburgh Approach; 7.1 Coding Rule Bases as Chromosomes; 7.2 Multi-chromosome Genomes (Coding Knowledge Bases); 7.3 Examples; Chapter 8 Genetic Fuzzy Rule-Based Systems Based on the Iterative Rule Learning Approach; 8.1 Coding the Fuzzy Rules; 8.2 Learning Fuzzy Rules under Competition; 8.3 Post-Processing: Refining Rule Bases under Cooperation; 8.4 Inducing Cooperation in the Fuzzy Rule Generation Stage; 8.5 Examples

Chapter 9 Other Genetic Fuzzy Rule-Based System Paradigms9.1 Designing Fuzzy Rule-Based Systems with Genetic Progamming; 9.2 Genetic Selection of Fuzzy Rule Sets; 9.3 Learning the Knowledge Base via the Genetic Derivation of the Data Base; 9.4 Other Genetic-Based Machine Learning Approaches; Chapter 10 Other Kinds of Evolutionary Fuzzy Systems; 10.1 Genetic Fuzzy Neural Networks; 10.2 Genetic Fuzzy Clustering; 10.3 Genetic Fuzzy Decision Trees; Chapter 11 Applications; 11.1 Classification; 11.2 System Modelling; 11.3 Control Systems; 11.4 Robotics; Bibliography; Acronyms; Index

Notes:

Description based upon print version of record.

Includes bibliographical references (p. 425-456) and index.

ISBN:

9786611956370

9781281956378

1281956376

9789812810731

9812810730

OCLC:

879023390