Studies in Fuzziness and Soft Computing : Studies in Fuzziness and Soft Computing - Danuta Rutkowska

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Studies in Fuzziness and Soft Computing

By: Danuta Rutkowska

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Hardcover | 14 December 2001

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Hardcover
308 Pages

Dimensions(cm)
23.5 x 15.88 x 1.91

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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.

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Paperback

Published: 21st October 2010

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Neural Networks and Neuro-Fuzzy Systems	p. 69
Neural Networks	p. 69
Model of an Artificial Neuron	p. 70
Multi-Layer Perceptron	p. 73
Back-Propagation Learning Method	p. 76
RBF Networks	p. 80
Supervised and Unsupervised Learning	p. 84
Competitive Learning	p. 85
Hebbian Learning Rule	p. 88
Kohonen's Self-Organizing Neural Network	p. 89
Learning Vector Quantization	p. 94
Other Types of Neural Networks	p. 97
Fuzzy Neural Networks	p. 98
Fuzzy Inference Neural Networks	p. 101
Neuro-Fuzzy Architectures Based on the Mamdani Approach	p. 105
Basic Architectures	p. 105
General Form of the Architectures	p. 109
Systems with Inference Based on Bounded Product	p. 114
Simplified Architectures	p. 116
Architectures Based on Other Defuzzification Methods	p. 119
COS-Based Architectures	p. 119
Neural Networks as Defuzzifiers	p. 122
Architectures of Systems with Non-Singleton Fuzzifier	p. 124
Neuro-Fuzzy Architectures Based on the Logical Approach	p. 127
Mathematical Descriptions of Implication-Based Systems	p. 127
NOCFS Architectures	p. 133
OCFS Architectures	p. 136
Performance Analysis	p. 145
Computer Simulations	p. 157
Function Approximation	p. 157
Control Examples	p. 158
Classification Problems	p. 160
Hybrid Learning Methods	p. 165
Gradient Learning Algorithms	p. 165
Learning of Fuzzy Systems	p. 166
Learning of Neuro-Fuzzy Systems	p. 171
FLiNN - Architecture Based Learning	p. 174
Genetic Algorithms	p. 175
Basic Genetic Algorithm	p. 175
Evolutionary Algorithms	p. 181
Clustering Algorithms	p. 185
Cluster Analysis	p. 185
Fuzzy Clustering	p. 189
Hybrid Learning	p. 191
Combinations of Gradient Methods, GAs, and Clustering Algorithms	p. 192
Hybrid Algorithms for Parameter Tuning	p. 194
Rule Generation	p. 195
Hybrid Learning Algorithms for Neuro-Fuzzy Systems	p. 198
Examples of Hybrid Learning Neuro-Fuzzy Systems	p. 199
Description of Two Hybrid Learning Algorithms for Rule Generation	p. 201
Medical Diagnosis Applications	p. 204
Intelligent Systems	p. 209
Artificial and Computational Intelligence	p. 209
Expert Systems	p. 212
Classical Expert Systems	p. 212
Fuzzy and Neural Expert Systems	p. 214
Intelligent Computational Systems	p. 217
Perception-Based Intelligent Systems	p. 220
Summary	p. 229
List of Figures	p. 233
List of Tables	p. 239
References	p. 241
Table of Contents provided by Publisher. All Rights Reserved.

Studies in Fuzziness and Soft Computing

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Hardcover

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