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E-raamat: Compensatory Genetic Fuzzy Neural Networks and Their Applications [World Scientific e-raamat]

(Univ Of South Florida, Usa), (Georgia State Univ, Usa)
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Compensatory Genetic Fuzzy Neural Networks and Their ApplicationsSuurem pilt
Teised raamatud teemal:
World Scientific e-raamatudRohkem infot World Scientific e-raamatute kohta
Raamatu kodulehekülg: http://www.worldscientific.com/worldscibooks/10.1142/3678#t=toc
This book presents a powerful hybrid intelligent system based on fuzzy logic, neural networks, genetic algorithms and related intelligent techniques. The new compensatory genetic fuzzy neural networks have been widely used in fuzzy control, nonlinear system modeling, compression of a fuzzy rule base, expansion of a sparse fuzzy rule base, fuzzy knowledge discovery, time series prediction, fuzzy games and pattern recognition. This effective soft computing system is able to perform both linguistic-word-level fuzzy reasoning and numerical-data-level information processing. The book also proposes various novel soft computing techniques.
Preface vii
1 Introduction
1(8)
1.1 Fuzzy Sets and Data Granularity
1(2)
1.2 Neural Networks and Knowledge Discovery
3(1)
1.3 Genetic Algorithms and Adaptive Optimization
4(1)
1.4 Soft Computing Systems and Computational Intelligence
5(2)
1.5 Main Issues
7(2)
2 Fuzzy Compensation Principles
9(32)
2.1 Fuzzy Yin-Yang Compensation
9(2)
2.2 Compensation of Fuzzy CNF and Fuzzy DNF
11(8)
2.2.1 Boolean Truth Table and Karnaugh Map
12(1)
2.2.2 Kaufmann's Fuzzy Truth Table and Fuzzy Map
13(2)
2.2.3 Universal Fuzzy Truth Table and AN D(m)(n) Map
15(4)
2.3 2-variable-2-dimensional CNFs and DNFs
19(2)
2.4 2-variable-m-dimensional CNFs and DNFs for m = 3,4
21(2)
2.5 Compensation of Universal Fuzzy CNF and Fuzzy DNF
23(17)
2.5.1 Boolean Logic
23(3)
2.5.2 General Fuzzy Logic
26(3)
2.5.3 m-dimensional Fuzzy CNFs and DNFs of a XXX a and a XXX a
29(2)
2.5.4 m-dimensional t-norm-t-conorm CNFs and DNFs
31(4)
2.5.5 Relations in Fuzzy and t-norm-t-conorm CNFs and DNFs
35(5)
2.6 Summary
40(1)
3 Normal Fuzzy Reasoning Methodology
41(16)
3.1 Primary Fuzzy Subsets
41(1)
3.2 The Variable-Input-Constant-Output (VICO) Problem
42(2)
3.3 Normal Fuzzy Reasoning (NFR)
44(5)
3.4 Normal Fuzzy Controllers
49(8)
4 Compensatory Genetic Fuzzy Neural Networks
57(14)
4.1 Introduction
57(1)
4.2 Fuzzy Neural Networks with Knowledge Discovery
58(3)
4.3 Heuristic Genetic Learning Algorithm for a FNNKD
61(6)
4.4 Feature Expressions of Trapezoidal-type Fuzzy Sets
67(1)
4.5 Crisp-Fuzzy Neural Networks (CFNN)
68(3)
5 Fuzzy Knowledge Rediscovery in Fuzzy Rule Bases
71(10)
5.1 Applicability of Various Defuzzification Techniques
71(6)
5.2 Nonlinear Function Approximation
77(4)
6 Fuzzy Cart-pole Balancing Control Systems
81(14)
6.1 Cart-pole Balancing Fuzzy Control Systems
81(5)
6.2 A Cart-pole Balancing System with Crisp Inputs and Outputs
86(4)
6.3 A Cart-pole Balancing System with Fuzzy Inputs and Outputs
90(5)
7 Fuzzy Knowledge Compression and Expansion
95(9)
7.1 Compression of Fuzzy Rule Bases
95(3)
7.2 Expansion of Fuzzy Rule Bases
98(6)
8 Highly Nonlinear System Modeling and Prediction
104(11)
8.1 Nonlinear Function Prediction
104(2)
8.2 Chaotic Time Series Prediction
106(6)
8.2.1 Wang's Fuzzy System and a FNNKD
108(1)
8.2.2 Effectiveness of the HGLA
109(1)
8.2.3 Analysis of Compensatory Degrees Gamma(k)
110(1)
8.2.4 Performance of Various Approaches
111(1)
8.3 Box and Jenkins's Gas Furnace Model Identification
112(3)
9 Fuzzy Moves in Fuzzy Games
115(30)
9.1 Introduction
115(1)
9.2 Fuzzy Moves
116(2)
9.3 Normal Fuzzy Reasoning for Fuzzy Moves
118(1)
9.4 Applicability of Various Methods
119(4)
9.4.1 Prisoners' Dilemma
119(4)
9.4.2 Applicability of Fuzzy Reasoning Methods
123(1)
9.5 Efficient Precise Decision Systems for Fuzzy Moves
123(3)
9.6 Typical Examples
126(1)
9.7 Fuzzy Moves in Prisoner's Dilemma
127(17)
9.7.1 Global Games and Global PDs
129(7)
9.7.2 Theory of Fuzzy Moves
136(4)
9.7.3 Fuzzy Moves in Global PDs
140(3)
9.7.4 Conclusions
143(1)
9.8 Summary
144(1)
10 Genetic Neuro-fuzzy Pattern Recognition
145(7)
10.1 Structure of a Genetic Fuzzy Neural Network
145(2)
10.2 Genetic-Algorithms-Based Self-Organizing Learning Algorithm
147(2)
10.3 Simulations
149(2)
10.4 Conclusions
151(1)
11 Constructive Approach to Modeling Fuzzy Systems
152(17)
11.1 Introduction
152(1)
11.2 A Normal-Fuzzy-Reasoning-Based Fuzzy System
153(1)
11.3 Various Single-Input-Single-Output (SISO) fuzzy systems
154(3)
11.4 Universal approximation
157(2)
11.5 A Piecewise nonlinear constructive algorithm
159(4)
11.6 Simulations
163(5)
11.6.1 A nonlinear function approximation
163(2)
11.6.2 Box and Jenkins's gas furnace model identification
165(1)
11.6.3 A chaotic system identification
166(2)
11.7 Conclusions
168(1)
12 Conclusions
169(4)
12.1 Main Conclusions
169(2)
12.2 Future Research and Development
171(2)
Bibliography 173(10)
Index 183
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