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. In a serial computer we have to form the scalar products (v + k q) as is evident from the basic equation (5.11). The same projection is needed when the computer consists of a parallel network with three layers, as shown in Figs. 7.2 and 7.3.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Bibliography and Comments
Learning of the Synaptic Strengths
H. Haken: Lectures given at the University of Stuttgart (1988)
Information and Information Gain
H. Haken: Information and Self-organization, Springer Ser. Syn. Vol. 40 ( Springer, Berlin, Heidelberg 1988 )
The Basic Construction Principle of a Synergetic Computer Revisited
H. Haken: Information and Self-organization, Springer Ser. Syn. Vol. 40 ( Springer, Berlin, Heidelberg 1988 )
Learning by Means of the Information Gain
H. Haken: Information and Self-organization,cited above
D.H. Ackley, G.E. Hinton, T. J. Sejnowski: A learning algorithm for Boltzmann machines: Cognitive Science 9, 147–169 (1985)
The numerical results and figures are due to R. Haas, Diplom Thesis, Stuttgart (1989)
A Learning Algorithm Based on a Gradient Dynamics
H. Haken, R. Haas, W. Banzhaf: Biol. Cybern. 62, 107 —111 (1989)
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 1991 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Haken, H. (1991). Learning Algorithms. In: Synergetic Computers and Cognition. Springer Series in Synergetics, vol 50. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-22450-2_10
Download citation
DOI: https://doi.org/10.1007/978-3-662-22450-2_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-22452-6
Online ISBN: 978-3-662-22450-2
eBook Packages: Springer Book Archive