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Negative Based Higher Order Systematic Satisfiability Logic With Hybrid Black Hole Algorithm In Enhancing Multiunit Discrete Hopfield Neural Network

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dc.contributor.authorRusdi, Nur ‘Afifah
dc.date.accessioned2026-05-11T03:23:52Z
dc.date.available2026-05-11T03:23:52Z
dc.date.issued2025-06
dc.description.abstractUnderstanding intelligence is crucial for developing advanced intelligent models. In pursuit of this goal, satisfiability logical representation in Discrete Hopfield Neural Network has provided new insight in understanding the behaviour of the data. However, the role of negation in understanding intelligence has been overlooked as negation is often associated with false outcome. Negative Based Higher Order Systematic Satisfiability Logic is proposed to promote the appearance of negative literals within the clauses. The proposed logic demonstrates optimal performance as compared to existing logical rules. To further improve the quality of the final neuron states, Hybrid Black Hole Algorithm is proposed to update the neuron states that satisfy the multi-objective functions. The newly proposed mechanism will be incorporated into the logic mining model known as Multi-unit Negative Based Higher Order Systematic Satisfiability Reverse Analysis.
dc.identifier.urihttps://erepo.usm.my/handle/123456789/24167
dc.language.isoen
dc.subjectNeural networks (Computer science)
dc.subjectAlgorithms
dc.titleNegative Based Higher Order Systematic Satisfiability Logic With Hybrid Black Hole Algorithm In Enhancing Multiunit Discrete Hopfield Neural Network
dc.typeResource Types::text::thesis::doctoral thesis
dspace.entity.typePublication
oairecerif.author.affiliationUniversiti Sains Malaysia
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