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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

Lyapunov Function Input Layer Middle Layer Information Gain Langevin Equation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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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

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