Fuzzy Modelling

1. Front Matter

   Pages i-xix

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2. Modelling with Fuzzy Sets

   1. Front Matter

      Pages 1-1

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   2. Fuzzy Models: Methodology, Design, Applications and Challenges

      • Witold Pedrycz

      Pages 3-22

3. Relational Models

   1. Front Matter

      Pages 23-23

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   2. Fundamentals of Fuzzy Relation Calculus

      • Siegfried Gottwald

      Pages 25-47

   3. Max-Min Relational Networks

      • A. Blanco, M. Delgado, I. Requena

      Pages 49-70

   4. Relational Calculus in Designing Fuzzy Petri Nets

      • Heloisa Scarpelli, Fernando Gomide

      Pages 71-89

   5. Prediction Using Relational Models

      • José Valente de Oliveira

      Pages 91-113

   6. Implementing a Fuzzy Relational Neural Network for Phonetic Automatic Speech Recognition

      • Carlos Alberto Reyes Garcia, Wyllis Bandler

      Pages 115-139

   7. Fuzzy Ecological Models

      • S. Marsili Libelli, P. Cianchi

      Pages 141-164

4. Fuzzy Neural Networks

   1. Front Matter

      Pages 165-165

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   2. Fuzzy Neural Networks: Capabilities

      • James J. Buckley, Esfandiar Eslami

      Pages 167-183

   3. Development of Fuzzy Neural Networks

      • Hisao Ishibuchi

      Pages 185-202

   4. Designing Neuro-Fuzzy Systems through Backpropagation

      • Detlef Nauck, Rudolf Kruse

      Pages 203-228

5. Rule-Based Modelling

   1. Front Matter

      Pages 229-229

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   2. The Foundations of Rule-Based Computations in Fuzzy Models

      • Abraham Kandel, Roberto Pacheco, Alejandro Martins, Suresh Khator

      Pages 231-263

   3. Evolutionary Learning of Fuzzy Rules: Competition and Cooperation

      • Andrea Bonarini

      Pages 265-283

   4. Logical Optimization of Rule-Based Models

      • Riccardo Rovatti

      Pages 285-311

   5. Rule Base Completion in Fuzzy Models

      • Thomas Sudkamp, Robert J. Hammell II

      Pages 313-330

   6. Hyperellipsoidal Clustering

      • Yoshiteru Nakamori, Mina Ryoke

      Pages 331-352

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.

• algorithms
• cognition
• complex system
• complex systems
• computer
• computer vision
• development
• modeling
• network
• neural networks
• optimization
• speech recognition

• Electrical and Computer Engineering, University of Manitoba, Winnipeg, Canada

  Witold Pedrycz

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