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Neuro-Fuzzy Architectures and Hybrid Learning : Studies in Fuzziness and Soft Computing - Danuta Rutkowska

Neuro-Fuzzy Architectures and Hybrid Learning

Studies in Fuzziness and Soft Computing

Hardcover

Published: 14th December 2001
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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.

Neural Networks and Neuro-Fuzzy Systemsp. 69
Neural Networksp. 69
Model of an Artificial Neuronp. 70
Multi-Layer Perceptronp. 73
Back-Propagation Learning Methodp. 76
RBF Networksp. 80
Supervised and Unsupervised Learningp. 84
Competitive Learningp. 85
Hebbian Learning Rulep. 88
Kohonen's Self-Organizing Neural Networkp. 89
Learning Vector Quantizationp. 94
Other Types of Neural Networksp. 97
Fuzzy Neural Networksp. 98
Fuzzy Inference Neural Networksp. 101
Neuro-Fuzzy Architectures Based on the Mamdani Approachp. 105
Basic Architecturesp. 105
General Form of the Architecturesp. 109
Systems with Inference Based on Bounded Productp. 114
Simplified Architecturesp. 116
Architectures Based on Other Defuzzification Methodsp. 119
COS-Based Architecturesp. 119
Neural Networks as Defuzzifiersp. 122
Architectures of Systems with Non-Singleton Fuzzifierp. 124
Neuro-Fuzzy Architectures Based on the Logical Approachp. 127
Mathematical Descriptions of Implication-Based Systemsp. 127
NOCFS Architecturesp. 133
OCFS Architecturesp. 136
Performance Analysisp. 145
Computer Simulationsp. 157
Function Approximationp. 157
Control Examplesp. 158
Classification Problemsp. 160
Hybrid Learning Methodsp. 165
Gradient Learning Algorithmsp. 165
Learning of Fuzzy Systemsp. 166
Learning of Neuro-Fuzzy Systemsp. 171
FLiNN - Architecture Based Learningp. 174
Genetic Algorithmsp. 175
Basic Genetic Algorithmp. 175
Evolutionary Algorithmsp. 181
Clustering Algorithmsp. 185
Cluster Analysisp. 185
Fuzzy Clusteringp. 189
Hybrid Learningp. 191
Combinations of Gradient Methods, GAs, and Clustering Algorithmsp. 192
Hybrid Algorithms for Parameter Tuningp. 194
Rule Generationp. 195
Hybrid Learning Algorithms for Neuro-Fuzzy Systemsp. 198
Examples of Hybrid Learning Neuro-Fuzzy Systemsp. 199
Description of Two Hybrid Learning Algorithms for Rule Generationp. 201
Medical Diagnosis Applicationsp. 204
Intelligent Systemsp. 209
Artificial and Computational Intelligencep. 209
Expert Systemsp. 212
Classical Expert Systemsp. 212
Fuzzy and Neural Expert Systemsp. 214
Intelligent Computational Systemsp. 217
Perception-Based Intelligent Systemsp. 220
Summaryp. 229
List of Figuresp. 233
List of Tablesp. 239
Referencesp. 241
Table of Contents provided by Publisher. All Rights Reserved.

ISBN: 9783790814385
ISBN-10: 3790814385
Series: Studies in Fuzziness and Soft Computing
Audience: Professional
Format: Hardcover
Language: English
Number Of Pages: 288
Published: 14th December 2001
Publisher: Springer-Verlag Berlin and Heidelberg Gmbh & Co. Kg
Country of Publication: DE
Dimensions (cm): 23.5 x 15.5  x 1.91
Weight (kg): 1.34