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Evolutionary Learning Algorithms for Neural Adaptive Control

  • Dimitris C. Dracopoulos

Part of the Perspectives in Neural Computing book series (PERSPECT.NEURAL)

Table of contents

  1. Front Matter
    Pages i-xi
  2. Dimitris C. Dracopoulos
    Pages 1-4
  3. Dimitris C. Dracopoulos
    Pages 5-21
  4. Dimitris C. Dracopoulos
    Pages 23-46
  5. Dimitris C. Dracopoulos
    Pages 47-70
  6. Dimitris C. Dracopoulos
    Pages 71-96
  7. Dimitris C. Dracopoulos
    Pages 97-109
  8. Dimitris C. Dracopoulos
    Pages 111-131
  9. Dimitris C. Dracopoulos
    Pages 133-163
  10. Dimitris C. Dracopoulos
    Pages 165-167
  11. Back Matter
    Pages 169-211

About this book

Introduction

Evolutionary Learning Algorithms for Neural Adaptive Control is an advanced textbook, which investigates how neural networks and genetic algorithms can be applied to difficult adaptive control problems which conventional results are either unable to solve , or for which they can not provide satisfactory results. It focuses on the principles involved, rather than on the modelling of the applications themselves, and therefore provides the reader with a good introduction to the fundamental issues involved.

Keywords

Adaptive control artificial neural network control evolution genetic algorithms learning modeling neural networks

Authors and affiliations

  • Dimitris C. Dracopoulos
    • 1
  1. 1.Department of Computer ScienceBrunel UniversityUxbridge, MiddlesexUK

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4471-0903-7
  • Copyright Information Springer-Verlag London Limited 1997
  • Publisher Name Springer, London
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-540-76161-7
  • Online ISBN 978-1-4471-0903-7
  • Series Print ISSN 1431-6854
  • Buy this book on publisher's site