Maximal domains of attraction in a Hopfield neural network with learning

Kevin L. Moore, D. Subbaram Naidu

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

4 Scopus citations

Abstract

In this paper we describe an approach to maximizing the domains of attraction for equilibria in a Hopfield neural network with learning. The concept of learning in a Hopfield net is introduced and a method is given to construct a hetero-associative memory using a Hopfield net that `learns' the correct weights required to store arbitrarily specified input/output pairs. By proper choice of the feedback gains in the weight update equation it is possible to maximize the domain of attraction for the stored equilibrium points, resulting in a robust associative memory.

Original languageEnglish (US)
Title of host publicationAmerican Control Conference
PublisherPubl by IEEE
Pages2894-2896
Number of pages3
ISBN (Print)0780308611, 9780780308619
DOIs
StatePublished - 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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    Moore, K. L., & Naidu, D. S. (1993). Maximal domains of attraction in a Hopfield neural network with learning. In American Control Conference (pp. 2894-2896). (American Control Conference). Publ by IEEE. https://doi.org/10.23919/acc.1993.4793428