Neuro-Fuzzy Architectures and Hybrid Learning / Edition 1

by Danuta Rutkowska

Overview

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 conception, 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.

Product Details

ISBN-13:	9783790825008
Publisher:	Physica-Verlag HD
Publication date:	12/15/2010
Series:	Studies in Fuzziness and Soft Computing , #85
Edition description:	Softcover reprint of hardcover 1st ed. 2002
Pages:	288
Product dimensions:	6.10(w) x 9.25(h) x 0.03(d)

Table of Contents

1 Introduction.- 2 Description of Fuzzy Inference Systems.- 2.1 Fuzzy Sets.- 2.2 Approximxate Reasoning.- 2.3 Fuzzy Systems.- 3 Neural Networks and Neuro-Fuzzy Systems.- 3.1 Neural Networks.- 3.2 Fuzzy Neural Networks.- 3.3 Fuzzy Inference Neural Networks.- 4 Neuro-Fuzzy Architectures Based on the Mamdani Approach.- 4.1 Basic Architectures.- 4.2 General Form of the Architectures.- 4.3 Systems with Inference Based on Bounded Product.- 4.4 Simplified Architectures.- 4.5 Architectures Based on Other Defuzzification Methods.- 4.6 Architectures of Systems with Non-Singleton Fuzzifier.- 5 Neuro-Fuzzy Architectures Based on the Logical Approach.- 5.1 Mathematical Descriptions of Implication-Based Systems.- 5.2 NOCFS Architectures.- 5.3 OCFS Architectures.- 5.4 Performance Analysis.- 5.5 Computer Simulations.- 6 Hybrid Learning Methods.- 6.1 Gradient Learning Algorithms.- 6.2 Genetic Algorithms.- 6.3 Clustering Algorithms.- 6.4 Hybrid Learning.- 6.5 Hybrid Learning Algorithms for Neuro-Fuzzy Systems.- 7 Intelligent Systems.- 7.1 Artificial and Computational Intelligence.- 7.2 Expert Systems.- 7.3 Intelligent Computational Systems.- 7.4 Perception-Based Intelligent Systems.- 8 Summary.- List of Figures.- List of Tables.- References.