Rusdi, Nur ‘Afifah (2025) Negative Based Higher Order Systematic Satisfiability Logic With Hybrid Black Hole Algorithm In Enhancing Multiunit Discrete Hopfield Neural Network. PhD thesis, Universiti Sains Malaysia.
![[img]](http://eprints.usm.my/style/images/fileicons/application_pdf.png) |
PDF
Download (454kB) |
Abstract
Understanding 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.
| Item Type: | Thesis (PhD) |
|---|---|
| Subjects: | Q Science > QA Mathematics > QA1-939 Mathematics |
| Divisions: | Pusat Pengajian Sains Matematik (School of Mathematical Sciences) > Thesis |
| Depositing User: | Mr Aizat Asmawi Abdul Rahim |
| Date Deposited: | 11 May 2026 03:31 |
| Last Modified: | 11 May 2026 03:31 |
| URI: | http://eprints.usm.my/id/eprint/64145 |
Actions (login required)
 |
View Item |
