Fuzzy Modelling: Paradigms and Practice / Edition 1

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Fuzzy Modelling: Paradigms and Practice provides an up-to-date and authoritative compendium of fuzzy models, identification algorithms and applications. Chapters in this book have been written by the leading scholars and researchers in their respective subject areas. Several of these chapters include both theoretical material and applications. The editor of this volume has organized and edited the chapters into a coherent and uniform framework.

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, etc., 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.

Fuzzy Modelling: Paradigms and Practice provides a wealth of specific fuzzy modelling paradigms, algorithms and tools used in systems modelling. Also included is a panoply of 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.

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Editorial Reviews

Provides recent information on fuzzy models, identification algorithms, and applications. Section I on relational models includes theory and case studies in areas such as speech recognition, prediction, and ecological systems. Section II on fuzzy neural networks covers fundamentals such as neurocomputing, explains the relationship between fuzzy systems and neural networks, and details architectures. Section III addresses design principles governing the development of rule-based models. Of interest to researchers and practitioners developing models of complex systems. Annotation c. Book News, Inc., Portland, OR (booknews.com)
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Product Details

Table of Contents

Part 1: Modelling with Fuzzy Sets. 1.1. Fuzzy Models: Methodology, Design, Applications, and Challenges; W. Pedrycz. Part 2: Relational Models. 2.1. Fundamentals of Fuzzy Relational Calculus; S. Gottwald. 2.2. Max-Min Relational Networks; A. Blanco, et al. 2.3. Relational Calculus in Designing Fuzzy Petri Networks; H. Scarpelli, F. Gomide. 2.4. Prediction in Relational Models; J. Valente de Oliveira. 2.5. Implementing a Fuzzy Relational Network for Phonetic Automatic Speech Recognition; C.A. Reyes-Garcia, W. Bandler. 2.6. Fuzzy Ecological Models; S. Marsili Libelli, P. Cianchi. Part 3: Fuzzy Neural Networks. 3.1. Fuzzy Neural Networks: Capabilities; J. Buckley, E. Eslani. 3.2. Development of Fuzzy Neural Networks; H. Ishibuchi. 3.3. Designing Fuzzy Neural Networks Through Backpropagation; D. Nauck, R. Kruse. Part 4: Rule-Based Modelling. 4.1. Foundations of Rule-based Computations in Fuzzy Models; A. Kandel, et al. 4.2. Evolutionary Learning of Rules: Competition and Cooperation; A. Bonarini. 4.3. Logical Optimization of Rule-based Models; R. Rovatti. 4.4. Interpretation and Completion of Fuzzy Rules; T. Sudkamp, R.J. Hammel II. 4.5 Hyperellipsoidal Clustering; Y. Nakamori, M. Ryoke. 4.6. Fuzzy Rule-based Models in Computer Vision; J.M. Keller, et al. 4.7. Forecasting in Rule-based Systems; A. Zardecki. Subject Index.

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