Logic Learning in Hopfield Networks

2008/04/25 by Saratha Sathasivam, Sathasivam, Saratha, Wan Ahmad Tajuddin Wan Abdullah +1
Computer Science · #Advanced Algebra and Logic #FOS: Computer and information sciences #Logic in Computer Science (cs.LO) #Neural Networks and Applications #Neural and Evolutionary Computing (cs.NE) #Rough Sets and Fuzzy Logic #cs.LO #cs.NE

paper · pdf · doi:10.48550/arxiv.0804.4075

To appear in Mod. Appl. Sci

arxiv created 2008/04/25 · openalex publication_date 2008/04/25 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Synaptic 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.

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