Advanced Fuzzy Systems Design and Applications / Edition 1

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Overview

This book presents a variety of recently developed methods for generating fuzzy rules from data with the help of neural networks and evolutionary algorithms. Special efforts have been put on dealing with knowledge incorporation into neural and evolutionary systems and knowledge extraction from data with the help of fuzzy logic. On the one hand, knowledge that is understandable to human beings can be extracted from data using evolutionary and learning methods by maintaining the interpretability of the generated fuzzy rules. On the other hand, a priori knowledge like expert knowledge and human preferences can be incorporated into evolution and learning, taking advantage of the knowledge representation capability of fuzzy rule systems and fuzzy preference models. Several engineering application examples in the fields of intelligent vehicle systems, process modeling and control and robotics are presented.
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Product Details

  • ISBN-13: 9783790825206
  • Publisher: Physica-Verlag HD
  • Publication date: 3/31/2013
  • Series: Studies in Fuzziness and Soft Computing Series , #112
  • Edition description: Softcover reprint of hardcover 1st ed. 2003
  • Edition number: 1
  • Pages: 272
  • Product dimensions: 6.14 (w) x 9.21 (h) x 0.60 (d)

Table of Contents

1. Fuzzy Sets and Fuzzy Systems.- 1.1 Basics of Fuzzy Sets.- 1.1.1 Fuzzy Sets.- 1.1.2 Fuzzy Operations.- 1.1.3 Fuzzy Relations.- 1.1.4 Measures of Fuzziness.- 1.1.5 Measures of Fuzzy Similarity.- 1.2 Fuzzy Rule Systems.- 1.2.1 Linguistic Variables and Linguistic Hedges.- 1.2.2 Fuzzy Rules for Modeling and Control.- 1.2.3 Mamdani Fuzzy Rule Systems.- 1.2.4 Takagi-Sugeno-Kang Fuzzy Rule Systems.- 1.2.5 Fuzzy Systems are Universal Approximators.- 1.3 Interpretability of Fuzzy Rule System.- 1.3.1 Introduction.- 1.3.2 The Properties of Membership Functions.- 1.3.3 Completeness of Fuzzy Partitions.- 1.3.4 Distinguishability of Fuzzy Partitions.- 1.3.5 Consistency of Fuzzy Rules.- 1.3.6 Completeness and Compactness of Rule Structure.- 1.4 Knowledge Processing with Fuzzy Logic.- 1.4.1 Knowledge Representation and Acquisition with IFTHEN Rules.- 1.4.2 Knowledge Representation with Fuzzy Preference Models.- 1.4.3 Fuzzy Group Decision Making.- 2. Evolutionary Algorithms.- 2.1 Introduction.- 2.2 Generic Evolutionary Algorithms.- 2.2.1 Representation.- 2.2.2 Recombination.- 2.2.3 Mutation.- 2.2.4 Selection.- 2.3 Adaptation and Self-Adaptation in Evolutionary Algorithms.- 2.3.1 Adaptation.- 2.3.2 Self-adaptation.- 2.4 Constraints Handling.- 2.5 Multi-objective Evolution.- 2.5.1 Weighted Aggregation Approaches.- 2.5.2 Population-based Non-Pareto Approaches.- 2.5.3 Pareto-based Approaches.- 2.5.4 Discussions.- 2.6 Evolution with Uncertain Fitness Functions.- 2.6.1 Noisy Fitness Functions.- 2.6.2 Approximate Fitness Functions.- 2.6.3 Robustness Considerations.- 2.7 Parallel Implementations.- 2.8 Summary.- 3. Artificial Neural Networks.- 3.1 Introduction.- 3.2 Feedforward Neural Network Models.- 3.2.1 Multilayer Perceptrons.- 3.2.2 Radial Basis Function Networks.- 3.3 Learning Algorithms.- 3.3.1 Supervised Learning.- 3.3.2 Unsupervised Learning.- 3.3.3 Reinforcement Learning.- 3.4 Improvement of Generalization.- 3.4.1 Heuristic Methods.- 3.4.2 Active Data Selection.- 3.4.3 Regularization.- 3.4.4 Network Ensembles.- 3.4.5 A Priori Knowledge.- 3.5 Rule Extraction from Neural Networks.- 3.5.1 Extraction of Symbolic Rules.- 3.5.2 Extraction of Fuzzy Rules.- 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.- 5.2.1 The Takagi-Sugeno-Kang Fuzzy Model.- 5.2.2 The Hybrid Neural Network Model.- 5.2.3 Training Algorithms.- 5.2.4 Interpretability Issues.- 5.3 Modeling and Control Using the Neurofuzzy System.- 5.3.1 Short-term Precipitation Prediction.- 5.3.2 Dynamic Robot Control.- 5.4 Neurofuzzy Control of Nonlinear Systems.- 5.4.1 Fuzzy Linearization.- 5.4.2 Neurofuzzy Identification of the Subsystems.- 5.4.3 Design of Controller.- 5.4.4 Stability Analysis.- 5.5 Summary.- 6. Evolutionary Design of Fuzzy Systems.- 6.1 Introduction.- 6.2 Evolutionary Design of Flexible Structured Fuzzy Controller..- 6.2.1 A Flexible Structured Fuzzy Controller.- 6.2.2 Parameter Optimization Using Evolution Strategies...- 6.2.3 Simulation Study.- 6.3 Evolutionary Optimization of Fuzzy Rules.- 6.3.1 Genetic Coding of Fuzzy Systems.- 6.3.2 Fitness Function.- 6.3.3 Evolutionary Fuzzy Modeling of Robot Dynamics.- 6.4 Fuzzy Systems Design for High-Dimensional Systems.- 6.4.1 Curse of Dimensionality.- 6.4.2 Flexible Fuzzy Partitions.- 6.4.3 Hierarchical Structures.- 6.4.4 Input Dimension Reduction.- 6.4.5 GA-Based Input Selection.- 6.5 Summary.- 7. Knowledge Discovery by Extracting Interpretable Fuzzy Rules.- 7.1 Introduction.- 7.1.1 Data, Information and Knowledge.- 7.1.2 Interpretability and Knowledge Extraction.- 7.2 Evolutionary Interpretable Fuzzy Rule Generation.- 7.2.1 Evolution Strategy for Mixed Parameter Optimization.- 7.2.2 Genetic Representation of Fuzzy Systems.- 7.2.3 Multiobjective Fuzzy Systems Optimization.- 7.2.4 An Example: Fuzzy Vehicle Distance Controller.- 7.3 Interactive Co-evolution for Fuzzy Rule Extraction.- 7.3.1 Interactive Evolution.- 7.3.2 Co-evolution.- 7.3.3 Interactive Co-evolution of Interpretable Fuzzy Systems.- 7.4 Fuzzy Rule Extraction from RBF Networks.- 7.4.1 Radial-Basis-Function Networks and Fuzzy Systems.- 7.4.2 Fuzzy Rule Extraction by Regularization.- 7.4.3 Application Examples.- 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.- 8.2.1 Adaptive Inverse Neural Control.- 8.2.2 Knowledge Incorporation in Adaptive Neural Control.- 8.3 Fuzzy Knowledge Incorporation By Regularization.- 8.3.1 Knowledge Representation with Fuzzy Rules.- 8.3.2 Regularized Learning.- 8.4 Fuzzy Knowledge as A Related Task in Learning.- 8.4.1 Learning Related Tasks.- 8.4.2 Fuzzy Knowledge as A Related Task.- 8.5 Simulation Studies.- 8.5.1 Regularized Learning.- 8.5.2 Multi-task Learning.- 8.6 Summary.- 9. Fuzzy Preferences Incorporation into Multi-objective Optimization.- 9.1 Multi-objective Optimization and Preferences Handling.- 9.1.1 Multi-objective Optimization.- 9.1.2 Incorporation of Fuzzy Preferences.- 9.2 Evolutionary Dynamic Weighted Aggregation.- 9.2.1 Conventional Weighted Aggregation for MOO.- 9.2.2 Dynamically Weighted Aggregation.- 9.2.3 Archiving of Pareto Solutions.- 9.2.4 Simulation Studies.- 9.2.5 Theoretical Analysis.- 9.3 Fuzzy Preferences Incorporation in MOO.- 9.3.1 Converting Fuzzy Preferences into Crisp Weights.- 9.3.2 Converting Fuzzy Preferences into Weight Intervals.- 9.4 Summary.- References.

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