Analysis of logic satisfiability in energy based discrete Hopfield neural network
Logic Satisfiability in conventional Discrete Hopfield Neural Network (DHNN) has suffered major issues such as the high potential to be trapped in suboptimal solutions. This cause problem to the network because suboptimal final neuron state that is trapped in a local minima solution will be disregar...
| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
American Institute of Physics
2024
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| Online Access: | http://psasir.upm.edu.my/id/eprint/121241/ http://psasir.upm.edu.my/id/eprint/121241/1/121241.pdf |
| _version_ | 1848868333737213952 |
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| author | Zamri, Nur Ezlin Mohd Kasihmuddin, Mohd Shareduwan Mansor, Mohd. Asyraf Marsani, Muhammad Fadhil Sathasivam, Saratha Mohd Jamaludin, Siti Zulaikha |
| author_facet | Zamri, Nur Ezlin Mohd Kasihmuddin, Mohd Shareduwan Mansor, Mohd. Asyraf Marsani, Muhammad Fadhil Sathasivam, Saratha Mohd Jamaludin, Siti Zulaikha |
| author_sort | Zamri, Nur Ezlin |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Logic Satisfiability in conventional Discrete Hopfield Neural Network (DHNN) has suffered major issues such as the high potential to be trapped in suboptimal solutions. This cause problem to the network because suboptimal final neuron state that is trapped in a local minima solution will be disregarded as a potential solution for any given optimization problem. Energy-Based DHNN has the advantage to move the suboptimal final neuron state into a potential global solution. This type of DHNN utilizes temperature to change the position of the solution until the network achieves a global minimum solution. In this paper, we proposed a comprehensive comparison between two conventional energy-based DHNN in doing 2 Satisfiability namely Mean Field Theory DHNN and Boltzmann DHNN. The proposed network will be compared with conventional DHNN using various performance metrics and parameter settings. Finally, results from the experiment suggest that with the right parameter setup, the performance of Boltzmann DHNN are approximately equal to the optimal Mean Field Theory DHNN. |
| first_indexed | 2025-11-15T14:50:44Z |
| format | Conference or Workshop Item |
| id | upm-121241 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:50:44Z |
| publishDate | 2024 |
| publisher | American Institute of Physics |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1212412025-10-29T08:35:45Z http://psasir.upm.edu.my/id/eprint/121241/ Analysis of logic satisfiability in energy based discrete Hopfield neural network Zamri, Nur Ezlin Mohd Kasihmuddin, Mohd Shareduwan Mansor, Mohd. Asyraf Marsani, Muhammad Fadhil Sathasivam, Saratha Mohd Jamaludin, Siti Zulaikha Logic Satisfiability in conventional Discrete Hopfield Neural Network (DHNN) has suffered major issues such as the high potential to be trapped in suboptimal solutions. This cause problem to the network because suboptimal final neuron state that is trapped in a local minima solution will be disregarded as a potential solution for any given optimization problem. Energy-Based DHNN has the advantage to move the suboptimal final neuron state into a potential global solution. This type of DHNN utilizes temperature to change the position of the solution until the network achieves a global minimum solution. In this paper, we proposed a comprehensive comparison between two conventional energy-based DHNN in doing 2 Satisfiability namely Mean Field Theory DHNN and Boltzmann DHNN. The proposed network will be compared with conventional DHNN using various performance metrics and parameter settings. Finally, results from the experiment suggest that with the right parameter setup, the performance of Boltzmann DHNN are approximately equal to the optimal Mean Field Theory DHNN. American Institute of Physics 2024 Conference or Workshop Item NonPeerReviewed text en http://psasir.upm.edu.my/id/eprint/121241/1/121241.pdf Zamri, Nur Ezlin and Mohd Kasihmuddin, Mohd Shareduwan and Mansor, Mohd. Asyraf and Marsani, Muhammad Fadhil and Sathasivam, Saratha and Mohd Jamaludin, Siti Zulaikha (2024) Analysis of logic satisfiability in energy based discrete Hopfield neural network. In: 3rd International Conference on Applied & Industrial Mathematics and Statistics 2022 (ICoAIMS 2022), 24-26 Aug. 2022 (pp. 1-7). https://pubs.aip.org/aip/acp/article-lookup/doi/10.1063/5.0194528 10.1063/5.0194528 |
| spellingShingle | Zamri, Nur Ezlin Mohd Kasihmuddin, Mohd Shareduwan Mansor, Mohd. Asyraf Marsani, Muhammad Fadhil Sathasivam, Saratha Mohd Jamaludin, Siti Zulaikha Analysis of logic satisfiability in energy based discrete Hopfield neural network |
| title | Analysis of logic satisfiability in energy based discrete Hopfield neural network |
| title_full | Analysis of logic satisfiability in energy based discrete Hopfield neural network |
| title_fullStr | Analysis of logic satisfiability in energy based discrete Hopfield neural network |
| title_full_unstemmed | Analysis of logic satisfiability in energy based discrete Hopfield neural network |
| title_short | Analysis of logic satisfiability in energy based discrete Hopfield neural network |
| title_sort | analysis of logic satisfiability in energy based discrete hopfield neural network |
| url | http://psasir.upm.edu.my/id/eprint/121241/ http://psasir.upm.edu.my/id/eprint/121241/ http://psasir.upm.edu.my/id/eprint/121241/ http://psasir.upm.edu.my/id/eprint/121241/1/121241.pdf |