Logic Learning in Hopfield Networks

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dc.creatorSathasivam, Saratha
dc.creatorAbdullah, Wan Ahmad Tajuddin Wan
dc.date2008-04-25
dc.date.accessioned2026-07-07T09:35:18Z
dc.date.available2026-07-07T09:35:18Z
dc.descriptionSynaptic weights for neurons in logic programming can be calculated either by using Hebbian learning or by Wan Abdullah's method. In other words, Hebbian learning for governing events corresponding to some respective program clauses is equivalent with learning using Wan Abdullah's method for the same respective program clauses. In this paper we will evaluate experimentally the equivalence between these two types of learning through computer simulations.
dc.descriptionTo appear in Mod. Appl. Sci
dc.identifierhttps://arxiv.org/abs/0804.4075
dc.identifierhttp://arxiv.org/abs/0804.4075
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/159790
dc.subjectLogic in Computer Science
dc.subjectNeural and Evolutionary Computing
dc.titleLogic Learning in Hopfield Networks
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