Gao, Yuan (2025) G-Type Random 3 Satisfiability With Discrete Hopfield Neural Network. PhD thesis, Universiti Sains Malaysia.
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Abstract
Logical relationships are crucial in artificial intelligence, especially in structuring data relations, as logical relationships provide a structured and formal framework. However, existing models often overlook the potential for flexible representations in satisfiability logical rules. To address this, this thesis proposes a novel logical rule called G-type Random 3 Satisfiability. This rule combines high-order, systematic, and non systematic logic to create a logical framework with high randomness and flexibility. When embedded into Discrete Hopfield neural networks, this logical rule significantly enhances the adaptability of the network in associative memory and complex logical task processing. To improve training efficiency, a novel Binary Optimal Solution Ant Colony Optimization algorithm is introduced as the learning mechanism. By refining the initialization process and update rules, the algorithm achieves a significantly faster convergence speed. For the retrieval phase, a new mutation strategy called Randomized Pairwise-Single Mutation is proposed, which enhances global solution diversity and further optimizes the quality of retrieval results.
| 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: | 26 Jun 2026 07:20 |
| Last Modified: | 26 Jun 2026 07:20 |
| URI: | http://eprints.usm.my/id/eprint/64491 |
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