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Fuzzy Modelling

Paradigms and Practice

  • Witold Pedrycz

Part of the International Series in Intelligent Technologies book series (ISIT, volume 7)

Table of contents

  1. Front Matter
    Pages i-xix
  2. Modelling with Fuzzy Sets

    1. Front Matter
      Pages 1-1
  3. Relational Models

    1. Front Matter
      Pages 23-23
    2. Siegfried Gottwald
      Pages 25-47
    3. A. Blanco, M. Delgado, I. Requena
      Pages 49-70
    4. Heloisa Scarpelli, Fernando Gomide
      Pages 71-89
    5. José Valente de Oliveira
      Pages 91-113
    6. Carlos Alberto Reyes Garcia, Wyllis Bandler
      Pages 115-139
    7. S. Marsili Libelli, P. Cianchi
      Pages 141-164
  4. Fuzzy Neural Networks

    1. Front Matter
      Pages 165-165
    2. James J. Buckley, Esfandiar Eslami
      Pages 167-183
    3. Hisao Ishibuchi
      Pages 185-202
    4. Detlef Nauck, Rudolf Kruse
      Pages 203-228
  5. Rule-Based Modelling

    1. Front Matter
      Pages 229-229
    2. Abraham Kandel, Roberto Pacheco, Alejandro Martins, Suresh Khator
      Pages 231-263
    3. Riccardo Rovatti
      Pages 285-311
    4. Thomas Sudkamp, Robert J. Hammell II
      Pages 313-330
    5. Yoshiteru Nakamori, Mina Ryoke
      Pages 331-352
    6. James M. Keller, Raghu Krishnapuram, Paul D. Gader, Young-Sik Choi
      Pages 353-374
    7. Andrew Zardecki
      Pages 375-391
  6. Back Matter
    Pages 393-394

About this book

Introduction

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.

Keywords

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

Editors and affiliations

  • Witold Pedrycz
    • 1
  1. 1.Electrical and Computer EngineeringUniversity of ManitobaWinnipegCanada

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4613-1365-6
  • Copyright Information Springer-Verlag US 1996
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4612-8589-2
  • Online ISBN 978-1-4613-1365-6
  • Series Print ISSN 1382-3434
  • Buy this book on publisher's site