Overview

This work provides a compendium of fuzzy models, identification algorithms and applications. Chapters have been written by leading scholars and researchers in their respective subject areas, and include both theoretical material and applications. The objective of this book is to provide researchers and practitioners involved in the development of models for complex systems with an understanding of fuzzy modelling, and an appreciation of what makes these models unique. The chapters are organized into three major parts covering relational models, fuzzy neural networks and rule-based models. The material on relational models includes theory along with a large number of implemented case studies, including some on speech recognition, prediction, and ecological systems. The part on fuzzy neural networks covers some fundamentals, such as neurocomputing, fuzzy neurocomputing, and so forth, identifies the nature of the relationship that exists between fuzzy systems and neural networks, and includes extensive coverage of their architectures. The last part addresses the main design principles governing the development of rule-based models. The book provides specific fuzzy modelling paradigms, algorithms and tools used in systems modelling. Also included are case studies from various computer, engineering and science disciplines. This should be a primary reference work for researchers and practitioners developing models of complex systems.

Full Product Details

Author:   Witold Pedrycz
Publisher:   Springer
Imprint:   Springer
Edition:   1996 ed.
Volume:   7
Dimensions:   Width: 15.50cm , Height: 2.30cm , Length: 23.50cm
Weight:   1.680kg
ISBN:  

9780792397038

ISBN 10:   0792397037
Pages:   394
Publication Date:   31 March 1996
Audience:   College/higher education ,  Professional and scholarly ,  Postgraduate, Research & Scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   In Print  
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Table of Contents

1: Modelling with Fuzzy Sets.- 1.1. Fuzzy Models: Methodology, Design, Applications, and Challenges.- 2: Relational Models.- 2.1. Fundamentals of Fuzzy Relational Calculus.- 2.2. Max-Min Relational Networks.- 2.3. Relational Calculus in Designing Fuzzy Petri Networks.- 2.4. Prediction in Relational Models.- 2.5 Implementing A Fuzzy Relational Network For Phonetic Automatic Speech Recognition.- 2.6 Fuzzy Ecological Models.- 3: Fuzzy Neural Networks.- 3.1. Fuzzy Neural Networks: Capabilities.- 3.2. Development of Fuzzy Neural Networks.- 3.3. Designing Fuzzy Neural Networks Through Backpropagation.- 4: Rule-Based Modelling.- 4.1. Foundations of Rule-Based Computations in Fuzzy Models.- 4.2. Evolutionary Learning of Rules Competition and Cooperation.- 4.3 Logical Optimization of Rule-Based Models.- 4.4 Interpretation and Completion of Fuzzy Rules.- 4.5 Hyperellipsoidal Clustering.- 4.6. Fuzzy Rule-Based Models in Computer Vision.- 4.7. Forecasting in Rule-Based Systems.

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