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Mutation Tabu Search Using Systematic Probabilistic Two Satisfiability In A Discrete Hopfield Neural Network

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dc.contributor.authorChen, Ju
dc.date.accessioned2026-06-12T07:37:24Z
dc.date.available2026-06-12T07:37:24Z
dc.date.issued2025-09
dc.description.abstractSatisfiability logic has been extensively applied in artificial neural network research. However, existing studies have not developed effective strategies for controlling the distribution of literals in the formulations. Therefore, this thesis proposes a Probabilistic 2 Satisfiability model that controls the quantity and position of positive literals through a positive rule applied to variables in second-order clauses.
dc.identifier.urihttps://erepo.usm.my/handle/123456789/24367
dc.language.isoen
dc.subjectMutation Tabu Search Using Systematic Probabilistic Two Satisfiability
dc.subjectDiscrete Hopfield Neural Network
dc.titleMutation Tabu Search Using Systematic Probabilistic Two Satisfiability In A Discrete Hopfield Neural Network
dc.typeResource Types::text::thesis::doctoral thesis
dspace.entity.typePublication
oairecerif.author.affiliationUniversiti Sains Malaysia
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