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