Approach to learning in Hopfield neural networks

Sudhakar Srinivasan, Kevin L. Moore, D. Subbaram Naidu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper we present some preliminary ideas for the design of a continuous nonlinear neural networks with `learning.' Specifically, we introduce the idea of learning in Hopfield recursive neural networks. The network is trained so that application of a set of inputs produces the desired set of outputs. A method is developed to determine the interconnecting weights for the network, so as to achieve the desired stable equilibrium points. Also, this method illustrates a way to `learn' the interconnecting weights that are not computed a priori. Conditions are obtained for the asymptotic stability of the equilibrium points. An illustrative simulation is presented.

Original languageEnglish (US)
Title of host publicationAmerican Control Conference
PublisherPubl by IEEE
Pages2892-2893
Number of pages2
ISBN (Print)0780308611, 9780780308619
DOIs
StatePublished - Jan 1 1993
EventProceedings of the 1993 American Control Conference - San Francisco, CA, USA
Duration: Jun 2 1993Jun 4 1993

Publication series

NameAmerican Control Conference

Other

OtherProceedings of the 1993 American Control Conference
CitySan Francisco, CA, USA
Period6/2/936/4/93

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