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Genetic Fuzzy Systems : Evolutionary Tuning and Learning of Fuzzy Knowledge Bases.
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 fuzz...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Otros Autores: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Singapore :
World Scientific Publishing Company,
2001.
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| Colección: | Advances in Fuzzy Systems-Applications and Theory.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- 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