Overview

The book provides an introduction to basic concepts as well as some recent advancements in fuzzy set theory, approximate reasoning, artificial neural networks and clustering methods. These methodologies create together the so-called soft computing, which is part of a computational approach to system intelligence. The book deals with an overview of fuzzy set theory, foundations for approximate reasoning principles, specific equivalence of inference results using logical conjunctive interpretations of if-then rules, supervised and unsupervised artificial neural networks, a new generalized conditional fuzzy clustering method, artificial neural networks-based fuzzy inference system with parameterized consequences in if-then rules, MATLAB m-files implementation of neuro-fuzzy systems, detailed study of neuro-fuzzy systems applications.

Full Product Details

Author:   Ernest Czogala ,  Jacek Leski
Publisher:   Physica-Verlag GmbH & Co
Imprint:   Physica-Verlag GmbH & Co
Edition:   illustrated edition
Volume:   47
Weight:   0.420kg
ISBN:  

9783790812893

ISBN 10:   3790812897
Pages:   195
Publication Date:   30 April 2000
Audience:   College/higher education ,  Undergraduate
Format:   Hardback
Publisher's Status:   Active
Availability:   Temporarily unavailable  
The supplier advises that this item is temporarily unavailable. It will be ordered for you and placed on backorder. Once it does come back in stock, we will ship it out to you.

Table of Contents

Classical Sets and Fuzzy Sets. Basic Definitions and Terminology: Classical Sets. Fuzzy Sets. Operations on Fuzzy Sets. Classification of t-Norms and t-Conorms. De Morgan Triple and Other Properties of t- and s-Norms. Parameterized t-, s-Norms and Negations. Fuzzy Relations. Cylindrical Extension and Projection of Fuzzy Sets. Extension Principle. Linguistic Variable. Summary.- Approximate Reasoning: Interpretation of Fuzzy Conditional Statement. An Approach to Axiomatic Definition of Fuzzy Implication. Compositional Rule of Inference. Fuzzy Reasoning. Canonical Fuzzy If-Then Rule. Aggregation Operation. Approximate Reasoning Using a Fuzzy Rule Base. Approximate Reasoning with Singletons. Fuzzifiers and Defuzzifiers. Equivalence of Approximate Reasoning Results Using Different Interpretations of If-Then Rules. Numerical Results. Summary.- Artificial Neural Networks: Introduction. Artificial Neural Networks Topologies. Learning in Artificial Neural Networks. Back-Propagation Learning Rule. Modifications of the Classic Back-Propagation Method. Optimization Methods in Neural Networks Learning. Networks with Output Linearly Depending on Parameters. Global Optimization Methods. Summary.- Unsupervised Learning. Clustering Methods: Introduction. Self-Organizing Feature Map. Vector Quantization and Learning Vector Quantization. An Overview of Clustering Methods. Fuzzy Clustering Methods. A Possibilistic Approach to Clustering. A New Generalized Weighted Conditional Fuzzy c-Means. Fuzzy Learning Vector Quantization. Cluster Validity. Summary.- Fuzzy Systems: Introduction. The Mamdani Fuzzy Systems. The Tagaki-Sugeno-Kang Fuzzy Systems. Fuzzy Systems with Parametrized Consequents. Summary.- Neuro-Fuzzy Systems: Introduction. Artificial Neural Network Based Fuzzy Inference Systems. Classifier Based On Neuro-Fuzzy System. ANNBFIS Optimization Using Deterministic Annealing. Further Investigations of Neuro-Fuzzy Systems. Summary.- Applications of Artificial Neural Network Based Fuzzy Inference System: Introduction. Application to Chaotic Time Series Prediction. Application to ECG Signal Compression. Application to Ripley's Synthetic Two-Class Data Classification. Application to the Recognition of Diabetes in Pima Indians. Application to the Iris Problem. Application to Monk's Problems. Application to System Identification. Application to Control. Application to Channel Equalization. Summary.

Reviews

Author Information

Tab Content 6

Author Website:  

Customer Reviews

Recent Reviews

No review item found!

Add your own review!

Countries Available

All regions