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Synergetic Computers and Cognition

A Top-Down Approach to Neural Nets

  • Hermann Haken

Part of the Springer Series in Synergetics book series (SSSYN, volume 50)

Table of contents

  1. Front Matter
    Pages I-IX
  2. Goal

    1. Hermann Haken
      Pages 1-6
  3. Synergetic Computers

    1. Front Matter
      Pages 7-7
    2. Hermann Haken
      Pages 9-17
    3. Hermann Haken
      Pages 18-19
    4. Hermann Haken
      Pages 20-35
    5. Hermann Haken
      Pages 56-59
    6. Hermann Haken
      Pages 84-120
    7. Hermann Haken
      Pages 121-129
  4. Cognition and Synergetic Computers

  5. Logical Operations and Outlook

    1. Front Matter
      Pages 189-189
    2. Hermann Haken
      Pages 195-210
    3. Hermann Haken
      Pages 211-213
  6. Back Matter
    Pages 215-226

About this book

Introduction

This book will be of interest to graduate students, researchers and teachers in the computer sciences, in the cognitive sciences andin physics. It provides the reader with a novel approach to the design and study of neural nets. The applicability of this approach is shown explicitly by means of realistic examples. In addition, detailed models of the cognitive abilities of humans are included and compared with the performance of the synergetic computer presented in this book. The work presented here would not have been possible without the important help of my coworkers. Dr. Arne Wunderlin has helped me in many respects over many years and has made essential contributions, in particular to the slaving principle of synergetics. Drs. Michael Bestehorn, Rudolf Friedrich and Wolfgang W eimer have applied the methods of synergetics to spontaneous pattern forma­ tion in fluids and have further developed these methods. Armirr Fuchs has not only implemented my algorithm on a VAX computer, but has also made his own important contributions, in particular to pattern recognition that is invariant with respect to translation, rotation, and scaling. Thomas Ditzinger, Richard Haas, and Robert Hönlinger have contributed within the work on their diploma theses to the application of our approach to a number of problems that are shared by humans and computers in the field of pattern recognition. I wish to thank all of them.

Keywords

Synergetic Computer algorithms associative memory cognition equilibrium invariant learning learning algorithm memory neural networks pattern pattern recognition perception standard model synergetics

Authors and affiliations

  • Hermann Haken
    • 1
    • 2
  1. 1.Institut für Theoretische Physik und SynergetikUniversität StuttgartStuttgart 80Fed. Rep. of Germany
  2. 2.Center for Complex SystemsFlorida Atlantic UniversityBoca RatonUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-662-22450-2
  • Copyright Information Springer-Verlag Berlin Heidelberg 1991
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-662-22452-6
  • Online ISBN 978-3-662-22450-2
  • Series Print ISSN 0172-7389
  • Buy this book on publisher's site