Learning Algorithms

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

Abstract

Learning is a central problem for neural and synergetic computers and in this chapter we shall present a number of learning algorithms. As we have seen in previous chapters, patterns are stored in the form of vectors v k . In order to perform pattern recognition, the formalism requires that the adjoint vectors v k + are known. These v k + occur in different ways depending on whether the formalism is realized on a serial computer or on a network.

Keywords

Entropy Haas Summing 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Reference

  1. H. Haken: Lectures given at the University of Stuttgart (1988)Google Scholar
  2. H. Haken: Information and Self-organization, Springer Ser. Syn. Vol. 40 ( Springer, Berlin, Heidelberg 1988 )Google Scholar
  3. H. Haken: Information and Self-organization,cited aboveGoogle Scholar
  4. For the special case of spin-glasses see:Google Scholar
  5. D.H. Ackley, G.E. Hinton, T.J. Sejnowski: A learning algorithm for Boltzmann machines: Cognitive Science 9, 147 —169 (1985)Google Scholar
  6. The numerical results and figures are due to R. Haas, Diplom Thesis, Stuttgart (1989)Google Scholar
  7. H. Haken, R. Haas, W. Banzhaf: Biol. Cybern. 62, 107 —111 (1989)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Hermann Haken
    • 1
  1. 1.Institut für Theoretische Physik und SynergetikUniversität StuttgartStuttgartGermany

Personalised recommendations