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Evolutionary Design of Intelligent Systems in Modeling, Simulation and Control

  • Oscar Castillo
  • Witold Pedrycz
  • Janusz Kacprzyk

Part of the Studies in Computational Intelligence book series (SCI, volume 257)

Table of contents

  1. Front Matter
  2. Intelligent Control

    1. Front Matter
      Pages 1-1
    2. Ricardo Martinez, Oscar Castillo, Luis T. Aguilar, Antonio Rodriguez
      Pages 17-31
    3. Ieroham S. Baruch, Rosalba Galvan-Guerra
      Pages 63-81
  3. Pattern Recognition

  4. Learning and Social Simulation

    1. Front Matter
      Pages 203-203
    2. Mario García-Valdez, Brunett Parra
      Pages 205-211
    3. Dora-Luz Flores, Antonio Rodríguez-Díaz, Juan R. Castro, Carelia Gaxiola
      Pages 213-225
    4. Carelia Gaxiola, Antonio Rodríguez-Díaz, Susan Jones, Manuel Castañón-Puga, Dora-Luz Flores
      Pages 227-241
  5. Robotics and Hardware Implementations

    1. Front Matter
      Pages 243-243
    2. Nohe R. Cazarez-Castro, Luis T. Aguilar, Oscar Castillo, Antonio Rodríguez-Dŕaz
      Pages 245-257
    3. Selene L. Cardenas-Maciel, Oscar Castillo, Luis T. Aguilar, Antonio Rodríguez-Díaz
      Pages 259-271
    4. Oscar Montiel, Roberto Sepúlveda, Yazmín Maldonado, Oscar Castillo
      Pages 273-293
    5. Roberto Sepúlveda, Oscar Montiel, José Olivas, Oscar Castillo
      Pages 295-308
    6. Roberto Sepúlveda, Oscar Montiel, Gabriel Lizárraga, Oscar Castillo
      Pages 309-325
  6. Back Matter

About this book

Introduction

The editors describe in this book, new methods for evolutionary design of intelligent systems using soft computing and their applications in modeling, simulation and control. Soft Computing (SC) consists of several intelligent computing paradigms, including fuzzy logic, neural networks, and evolutionary algorithms, which can be used to produce powerful hybrid intelligent systems. The book is organized in four main parts, which contain a group of papers around a similar subject. The first part consists of papers with the main theme of evolutionary design of fuzzy systems in intelligent control, which consists of papers that propose new methods for designing and optimizing intelligent controllers for different applications. The second part contains papers with the main theme of evolutionary design of intelligent systems for pattern recognition applications, which are basically papers using evolutionary algorithms for optimizing modular neural networks with fuzzy systems for response integration, for achieving pattern recognition in different applications. The third part contains papers with the themes of models for learning and social simulation, which are papers that apply intelligent systems to the problems of designing learning objects and social agents. The fourth part contains papers that deal with intelligent systems in robotics applications and hardware implementations.

Keywords

agents algorithms cognition control evolution fuzzy fuzzy logic learning modeling neural network neural networks pattern recognition robot robotics simulation

Editors and affiliations

  • Oscar Castillo
    • 1
  • Witold Pedrycz
    • 2
  • Janusz Kacprzyk
    • 3
  1. 1.Department of Computer ScienceTijuana Institute of TechnologyChula VistaUSA
  2. 2.Dept. Electrical and Computer EngineeringUniversity of AlbertaEdmontonCanada
  3. 3.Systems Research InstitutePolish Academy of SciencesWarszawaPoland

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-04514-1
  • Copyright Information Springer-Verlag Berlin Heidelberg 2009
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-642-04513-4
  • Online ISBN 978-3-642-04514-1
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • Buy this book on publisher's site