rent-now

Rent More, Save More! Use code: ECRENTAL

5% off 1 book, 7% off 2 books, 10% off 3+ books

9783790815375

Advanced Fuzzy Systems Design and Applications

by
  • ISBN13:

    9783790815375

  • ISBN10:

    3790815373

  • Format: Hardcover
  • Copyright: 2003-03-01
  • Publisher: Physica Verlag
  • Purchase Benefits
  • Free Shipping Icon Free Shipping On Orders Over $35!
    Your order must be $35 or more to qualify for free economy shipping. Bulk sales, PO's, Marketplace items, eBooks and apparel do not qualify for this offer.
  • eCampus.com Logo Get Rewarded for Ordering Your Textbooks! Enroll Now
  • Write a Review Icon Write a Review
List Price: $169.99 Save up to $134.35
  • Digital
    $77.22*
    Add to Cart

    DURATION
    PRICE
    *To support the delivery of the digital material to you, a digital delivery fee of $3.99 will be charged on each digital item.

Summary

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 prioriknowledge 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.

Table of Contents

Fuzzy Sets and Fuzzy Systems
1(48)
Basics of Fuzzy Sets
1(15)
Fuzzy Sets
1(6)
Fuzzy Operations
7(3)
Fuzzy Relations
10(3)
Measures of Fuzziness
13(2)
Measures of Fuzzy Similarity
15(1)
Fuzzy Rule Systems
16(13)
Linguistic Variables and Linguistic Hedges
16(3)
Fuzzy Rules for Modeling and Control
19(6)
Mamdani Fuzzy Rule Systems
25(1)
Takagi-Sugeno-Kang Fuzzy Rule Systems
26(1)
Fuzzy Systems are Universal Approximators
27(2)
Interpretability of Fuzzy Rule System
29(8)
Introduction
29(1)
The Properties of Membership Functions
30(1)
Completeness of Fuzzy Partitions
30(3)
Distinguishability of Fuzzy Partitions
33(1)
Consistency of Fuzzy Rules
34(3)
Completeness and Compactness of Rule Structure
37(1)
Knowledge Processing with Fuzzy Logic
37(12)
Knowledge Representation and Acquisition with IFTHEN Rules
37(5)
Knowledge Representation with Fuzzy Preference Models
42(3)
Fuzzy Group Decision Making
45(4)
Evolutionary Algorithms
49(24)
Introduction
49(1)
Generic Evolutionary Algorithms
49(6)
Representation
50(3)
Recombination
53(1)
Mutation
54(1)
Selection
55(1)
Adaptation and Self-Adaptation in Evolutionary Algorithms
55(3)
Adaptation
55(1)
Self-adaptation
56(2)
Constraints Handling
58(2)
Multi-objective Evolution
60(4)
Weighted Aggregation Approaches
61(1)
Population-based Non-Pareto Approaches
62(1)
Pareto-based Approaches
62(1)
Discussions
63(1)
Evolution with Uncertain Fitness Functions
64(5)
Noisy Fitness Functions
64(1)
Approximate Fitness Functions
64(4)
Robustness Considerations
68(1)
Parallel Implementations
69(1)
Summary
70(3)
Artificial Neural Networks
73(20)
Introduction
73(1)
Feedforward Neural Network Models
73(2)
Multilayer Perceptrons
74(1)
Radial Basis Function Networks
75(1)
Learning Algorithms
75(5)
Supervised Learning
76(2)
Unsupervised Learning
78(1)
Reinforcement Learning
79(1)
Improvement of Generalization
80(6)
Heuristic Methods
81(1)
Active Data Selection
81(1)
Regularization
82(2)
Network Ensembles
84(1)
A Priori Knowledge
85(1)
Rule Extraction from Neural Networks
86(3)
Extraction of Symbolic Rules
86(1)
Extraction of Fuzzy Rules
87(2)
Interaction between Evolution and Learning
89(1)
Summary
90(3)
Conventional Data-driven Fuzzy Systems Design
93(18)
Introduction
93(1)
Fuzzy Inference Based Method
94(6)
Wang-Mendel's Method
100(2)
A Direct Method
102(3)
An Adaptive Fuzzy Optimal Controller
105(5)
Summary
110(1)
Neural Network Based Fuzzy Systems Design
111(32)
Neurofuzzy Systems
111(3)
The Pi-sigma Neurofuzzy Model
114(9)
The Takagi-Sugeno-Kang Fuzzy Model
114(1)
The Hybrid Neural Network Model
115(1)
Training Algorithms
116(4)
Interpretability Issues
120(3)
Modeling and Control Using the Neurofuzzy System
123(7)
Short-term Precipitation Prediction
123(1)
Dynamic Robot Control
124(6)
Neurofuzzy Control of Nonlinear Systems
130(11)
Fuzzy Linearization
132(3)
Neurofuzzy Identification of the Subsystems
135(2)
Design of Controller
137(1)
Stability Analysis
138(3)
Summary
141(2)
Evolutionary Design of Fuzzy Systems
143(30)
Introduction
143(2)
Evolutionary Design of Flexible Structured Fuzzy Controller
145(3)
A Flexible Structured Fuzzy Controller
145(1)
Parameter Optimization Using Evolution Strategies
146(1)
Simulation Study
147(1)
Evolutionary Optimization of Fuzzy Rules
148(12)
Genetic Coding of Fuzzy Systems
148(4)
Fitness Function
152(1)
Evolutionary Fuzzy Modeling of Robot Dynamics
153(7)
Fuzzy Systems Design for High-Dimensional Systems
160(11)
Curse of Dimensionality
160(1)
Flexible Fuzzy Partitions
161(2)
Hierarchical Structures
163(1)
Input Dimension Reduction
164(5)
GA-Based Input Selection
169(2)
Summary
171(2)
Knowledge Discovery by Extracting Interpretable Fuzzy Rules
173(32)
Introduction
173(2)
Data, Information and Knowledge
173(1)
Interpretability and Knowledge Extraction
174(1)
Evolutionary Interpretable Fuzzy Rule Generation
175(9)
Evolution Strategy for Mixed Parameter Optimization
176(1)
Genetic Representation of Fuzzy Systems
177(1)
Multiobjective Fuzzy Systems Optimization
178(2)
An Example: Fuzzy Vehicle Distance Controller
180(4)
Interactive Co-evolution for Fuzzy Rule Extraction
184(3)
Interactive Evolution
184(2)
Co-evolution
186(1)
Interactive Co-evolution of Interpretable Fuzzy Systems
186(1)
Fuzzy Rule Extraction from RBF Networks
187(16)
Radial-Basis-Function Networks and Fuzzy Systems
187(4)
Fuzzy Rule Extraction by Regularization
191(5)
Application Examples
196(7)
Summary
203(2)
Fuzzy Knowledge Incorporation into Neural Networks
205(18)
Data and A Priori Knowledge
205(2)
Knowledge Incorporation in Neural Networks for Control
207(3)
Adaptive Inverse Neural Control
207(1)
Knowledge Incorporation in Adaptive Neural Control
208(2)
Fuzzy Knowledge Incorporation By Regularization
210(6)
Knowledge Representation with Fuzzy Rules
210(3)
Regularized Learning
213(3)
Fuzzy Knowledge as A Related Task in Learning
216(1)
Learning Related Tasks
216(1)
Fuzzy Knowledge as A Related Task
216(1)
Simulation Studies
217(4)
Regularized Learning
218(1)
Multi-task Learning
219(2)
Summary
221(2)
Fuzzy Preferences Incorporation into Multi-objective Optimization
223(45)
Multi-objective Optimization and Preferences Handling
223(3)
Multi-objective Optimization
223(2)
Incorporation of Fuzzy Preferences
225(1)
Evolutionary Dynamic Weighted Aggregation
226(21)
Conventional Weighted Aggregation for MOO
227(1)
Dynamically Weighted Aggregation
228(2)
Archiving of Pareto Solutions
230(1)
Simulation Studies
230(7)
Theoretical Analysis
237(10)
Fuzzy Preferences Incorporation in MOO
247(5)
Converting Fuzzy Preferences into Crisp Weights
247(2)
Converting Fuzzy Preferences into Weight Intervals
249(3)
Summary
252(16)
References 268(1)
Index 269

Supplemental Materials

What is included with this book?

The New copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any access cards, study guides, lab manuals, CDs, etc.

The Used, Rental and eBook copies of this book are not guaranteed to include any supplemental materials. Typically, only the book itself is included. This is true even if the title states it includes any access cards, study guides, lab manuals, CDs, etc.

Rewards Program