Neuro-Fuzzy Architectures and Hybrid Learning

Front Cover
Springer Science & Business Media, Dec 14, 2001 - Computers - 288 pages
The advent of the computer age has set in motion a profound shift in our perception of science -its structure, its aims and its evolution. Traditionally, the principal domains of science were, and are, considered to be mathe matics, physics, chemistry, biology, astronomy and related disciplines. But today, and to an increasing extent, scientific progress is being driven by a quest for machine intelligence - for systems which possess a high MIQ (Machine IQ) and can perform a wide variety of physical and mental tasks with minimal human intervention. The role model for intelligent systems is the human mind. The influ ence of the human mind as a role model is clearly visible in the methodolo gies which have emerged, mainly during the past two decades, for the con ception, design and utilization of intelligent systems. At the center of these methodologies are fuzzy logic (FL); neurocomputing (NC); evolutionary computing (EC); probabilistic computing (PC); chaotic computing (CC); and machine learning (ML). Collectively, these methodologies constitute what is called soft computing (SC). In this perspective, soft computing is basically a coalition of methodologies which collectively provide a body of concepts and techniques for automation of reasoning and decision-making in an environment of imprecision, uncertainty and partial truth.
 

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Contents

Introduction
1
Description of Fuzzy Inference Systems
5
212 Operations on Fuzzy Sets
12
213 Fuzzy Relations
19
214 Operations on Fuzzy Relations
22
22 Approximate Reasoning
25
222 Implications
27
223 Linguistic Variables
29
46 Architectures of Systems with NonSingleton Fuzzifier
124
NeuroFuzzy Architectures Based on the Logical Approach
127
52 NOCFS Architectures
133
53 OCFS Architectures
136
54 Performance Analysis
145
55 Computer Simulations
157
552 Control Examples
158
553 Classification Problems
160

224 Calculus of Fuzzy Rules
34
225 Granulation and Fuzzy Graphs
37
226 Computing with Words
41
23 Fuzzy Systems
43
231 RuleBased Fuzzy Logic Systems
44
232 The Mamdani and Logical Approaches to Fuzzy Inference
49
233 Fuzzy Systems Based on the Mamdani Approach
51
234 Fuzzy Systems Based on the Logical Approach
60
Neural Networks and NeuroFuzzy Systems
69
311 Model of an Artificial Neuron
70
312 MultiLayer Perceptron
73
313 BackPropagation Learning Method
76
314 RBF Networks
80
315 Supervised and Unsupervised Learning
84
316 Competitive Learning
85
317 Hebbian Learning Rule
88
318 Kohonens SelfOrganizing Neural Network
89
319 Learning Vector Quantization
94
3110 Other Types of Neural Networks
97
32 Fuzzy Neural Networks
98
33 Fuzzy Inference Neural Networks
101
NeuroFuzzy Architectures Based on the Mamdani Approach
105
42 General Form of the Architectures
109
43 Systems with Inference Based on Bounded Product
114
44 Simplified Architectures
116
45 Architectures Based on Other Defuzzification Methods
119
452 Neural Networks as Defuzzifiers
122
Hybrid Learning Methods
165
611 Learning of Fuzzy Systems
166
612 Learning of NeuroFuzzy Systems
171
613 FLiNN Architecture Based Learning
174
62 Genetic Algorithms
175
622 Evolutionary Algorithms
181
63 Clustering Algorithms
185
632 Fuzzy Clustering
189
64 Hybrid Learning
191
641 Combinations of Gradient Methods GAs and Clustering Algorithms
192
642 Hybrid Algorithms for Parameter Tuning
194
643 Rule Generation
195
65 Hybrid Learning Algorithms for NeuroFuzzy Systems
198
651 Examples of Hybrid Learning NeuroFuzzy Systems
199
652 Description of Two Hybrid Learning Algorithms for Rule Generation
201
653 Medical Diagnosis Applications
204
Intelligent Systems
209
72 Expert Systems
212
722 Fuzzy and Neural Expert Systems
214
73 Intelligent Computational Systems
217
74 PerceptionBased Intelligent Systems
220
Summary
229
List of Figures
233
List of Tables
239
References
241
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