Advanced Fuzzy Systems Design and Applications

• Κύριος συγγραφέας: Jin, Yaochu (Συγγραφέας, http://id.loc.gov/vocabulary/relators/aut)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Heidelberg : Physica-Verlag HD : Imprint: Physica, 2003.
• Έκδοση: 1st ed. 2003.
• Σειρά: Studies in Fuzziness and Soft Computing, 112
• Θέματα: Computer programming.; Artificial intelligence.; Mathematical logic.; Data structures (Computer science).; Algorithms.; Programming Techniques.; Artificial Intelligence.; Mathematical Logic and Foundations.; Data Structures and Information Theory.; Algorithm Analysis and Problem Complexity.

Fuzzy rule systems have found a wide range of applications in many fields of science and technology. Traditionally, fuzzy rules are generated from human expert knowledge or human heuristics for relatively simple systems. In the last few years, data-driven fuzzy rule generation has been very active....

Πίνακας περιεχομένων:

• 1. Fuzzy Sets and Fuzzy Systems
• 1.1 Basics of Fuzzy Sets
• 1.2 Fuzzy Rule Systems
• 1.3 Interpretability of Fuzzy Rule System
• 1.4 Knowledge Processing with Fuzzy Logic
• 2. Evolutionary Algorithms
• 2.1 Introduction
• 2.2 Generic Evolutionary Algorithms
• 2.3 Adaptation and Self-Adaptation in Evolutionary Algorithms
• 2.4 Constraints Handling
• 2.5 Multi-objective Evolution
• 2.6 Evolution with Uncertain Fitness Functions
• 2.7 Parallel Implementations
• 2.8 Summary
• 3. Artificial Neural Networks
• 3.1 Introduction
• 3.2 Feedforward Neural Network Models
• 3.3 Learning Algorithms
• 3.4 Improvement of Generalization
• 3.5 Rule Extraction from Neural Networks
• 3.6 Interaction between Evolution and Learning
• 3.7 Summary
• 4. Conventional Data-driven Fuzzy Systems Design
• 4.1 Introduction
• 4.2 Fuzzy Inference Based Method
• 4.3 Wang-Mendel's Method
• 4.4 A Direct Method
• 4.5 An Adaptive Fuzzy Optimal Controller
• 4.6 Summary
• 5.Neural Network Based Fuzzy Systems Design
• 5.1 Neurofuzzy Systems
• 5.2 The Pi-sigma Neurofuzzy Model
• 5.3 Modeling and Control Using the Neurofuzzy System
• 5.4 Neurofuzzy Control of Nonlinear Systems
• 5.5 Summary
• 6. Evolutionary Design of Fuzzy Systems
• 6.1 Introduction
• 6.2 Evolutionary Design of Flexible Structured Fuzzy Controller.
• 6.3 Evolutionary Optimization of Fuzzy Rules
• 6.4 Fuzzy Systems Design for High-Dimensional Systems
• 6.5 Summary
• 7. Knowledge Discovery by Extracting Interpretable Fuzzy Rules
• 7.1 Introduction
• 7.2 Evolutionary Interpretable Fuzzy Rule Generation
• 7.3 Interactive Co-evolution for Fuzzy Rule Extraction
• 7.4 Fuzzy Rule Extraction from RBF Networks
• 7.5 Summary
• 8. Fuzzy Knowledge Incorporation into Neural Networks
• 8.1 Data and A Priori Knowledge
• 8.2 Knowledge Incorporation in Neural Networks for Control
• 8.3 Fuzzy Knowledge Incorporation By Regularization
• 8.4 Fuzzy Knowledge as A Related Task in Learning
• 8.5 Simulation Studies
• 8.6 Summary
• 9. Fuzzy Preferences Incorporation into Multi-objective Optimization
• 9.1 Multi-objective Optimization and Preferences Handling
• 9.2 Evolutionary Dynamic Weighted Aggregation
• 9.3 Fuzzy Preferences Incorporation in MOO
• 9.4 Summary
• References.