Accuracy Improvements in Linguistic Fuzzy Modeling

  • Jorge Casillas
  • Oscar Cordón
  • Francisco Herrera
  • Luis Magdalena

Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 129)

Table of contents

  1. Front Matter
    Pages I-XII
  2. Overview

    1. Front Matter
      Pages 1-1
    2. Jorge Casillas, Oscar Cordón, Francisco Herrera, Luis Magdalena
      Pages 3-24
  3. Accuracy Improvements Constrained by Interpretability Criteria

  4. Extending the Modeling Process to Improve the Accuracy

    1. Front Matter
      Pages 163-163
    2. Chuen-Yau Chen, Bin-Da Liu
      Pages 165-192
  5. Extending the Model Structure to Improve the Accuracy

    1. Front Matter
      Pages 247-247
    2. Rafael Alcalá, Oscar Cordón, Francisco Herrera, Igor Zwir
      Pages 277-301
    3. Pablo Carmona, Juan Luis Castro, Jose Jesus Castro-Schez, Manuel Laguia
      Pages 302-337
    4. Tzung-Pei Hong, Ching-Hung Wang, Shian-Shyong Tseng
      Pages 338-365

About this book


Fuzzy modeling usually comes with two contradictory requirements: interpretability, which is the capability to express the real system behavior in a comprehensible way, and accuracy, which is the capability to faithfully represent the real system. In this framework, one of the most important areas is linguistic fuzzy modeling, where the legibility of the obtained model is the main objective. This task is usually developed by means of linguistic (Mamdani) fuzzy rule-based systems. An active research area is oriented towards the use of new techniques and structures to extend the classical, rigid linguistic fuzzy modeling with the main aim of increasing its precision degree. Traditionally, this accuracy improvement has been carried out without considering the corresponding interpretability loss. Currently, new trends have been proposed trying to preserve the linguistic fuzzy model description power during the optimization process. Written by leading experts in the field, this volume collects some representative researcher that pursue this approach.


algorithm algorithms classification complexity construction evolutionary algorithm fuzzy fuzzy system learning model modeling optimization proving

Editors and affiliations

  • Jorge Casillas
    • 1
  • Oscar Cordón
    • 1
  • Francisco Herrera
    • 1
  • Luis Magdalena
    • 2
  1. 1.Dpto. Ciencias de la Computación e Inteligencia Artificial, Escuela Técnica Superior de Ingeniería InformáticaUniversidad de GranadaGranadaSpain
  2. 2.Dpto. Matemáticas Aplicadas a las Tecnologías de la Información, Escuela Técnica Superior de Ingenieros de TelecomunicaciónUniversidad Politécnica de MadridMadridSpain

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag Berlin Heidelberg 2003
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-642-05703-8
  • Online ISBN 978-3-540-37058-1
  • Series Print ISSN 1434-9922
  • Series Online ISSN 1860-0808
  • Buy this book on publisher's site