Discrete hopfield neural network in restricted maximum k-satisfiability logic programming
Maximum k-Satisfiability (MAX-kSAT) consists of the most consistent interpretation that generate the maximum number of satisfied clauses. MAX-kSAT is an important logic representation in logic programming since not all combinatorial problem is satisfiable in nature. This paper presents Hopfield Neur...
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my-ukm.journal.121392018-09-25T01:34:20Z http://journalarticle.ukm.my/12139/ Discrete hopfield neural network in restricted maximum k-satisfiability logic programming Mohd Shareduwan Mohd Kasihmuddin, Mohd Asyraf Mansor, Saratha Sathasivam, Maximum k-Satisfiability (MAX-kSAT) consists of the most consistent interpretation that generate the maximum number of satisfied clauses. MAX-kSAT is an important logic representation in logic programming since not all combinatorial problem is satisfiable in nature. This paper presents Hopfield Neural Network based on MAX-kSAT logical rule. Learning of Hopfield Neural Network will be integrated with Wan Abdullah method and Sathasivam relaxation method to obtain the correct final state of the neurons. The computer simulation shows that MAX-kSAT can be embedded optimally in Hopfield Neural Network. Penerbit Universiti Kebangsaan Malaysia 2018-06 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/12139/1/30%20Mohd%20Shareduwan%20Mohd%20Kasihmuddin.pdf Mohd Shareduwan Mohd Kasihmuddin, and Mohd Asyraf Mansor, and Saratha Sathasivam, (2018) Discrete hopfield neural network in restricted maximum k-satisfiability logic programming. Sains Malaysiana, 47 (6). pp. 1327-1335. ISSN 0126-6039 http://www.ukm.my/jsm/english_journals/vol47num6_2018/contentsVol47num6_2018.html |
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Maximum k-Satisfiability (MAX-kSAT) consists of the most consistent interpretation that generate the maximum number of satisfied clauses. MAX-kSAT is an important logic representation in logic programming since not all combinatorial problem is satisfiable in nature. This paper presents Hopfield Neural Network based on MAX-kSAT logical rule. Learning of Hopfield Neural Network will be integrated with Wan Abdullah method and Sathasivam relaxation method to obtain the correct final state of the neurons. The computer simulation shows that MAX-kSAT can be embedded optimally in Hopfield Neural Network. |
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Mohd Shareduwan Mohd Kasihmuddin, Mohd Asyraf Mansor, Saratha Sathasivam, |
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Mohd Shareduwan Mohd Kasihmuddin, Mohd Asyraf Mansor, Saratha Sathasivam, Discrete hopfield neural network in restricted maximum k-satisfiability logic programming |
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Mohd Shareduwan Mohd Kasihmuddin, Mohd Asyraf Mansor, Saratha Sathasivam, |
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Mohd Shareduwan Mohd Kasihmuddin, |
title |
Discrete hopfield neural network in restricted maximum
k-satisfiability logic programming |
title_short |
Discrete hopfield neural network in restricted maximum
k-satisfiability logic programming |
title_full |
Discrete hopfield neural network in restricted maximum
k-satisfiability logic programming |
title_fullStr |
Discrete hopfield neural network in restricted maximum
k-satisfiability logic programming |
title_full_unstemmed |
Discrete hopfield neural network in restricted maximum
k-satisfiability logic programming |
title_sort |
discrete hopfield neural network in restricted maximum
k-satisfiability logic programming |
publisher |
Penerbit Universiti Kebangsaan Malaysia |
publishDate |
2018 |
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http://journalarticle.ukm.my/12139/1/30%20Mohd%20Shareduwan%20Mohd%20Kasihmuddin.pdf http://journalarticle.ukm.my/12139/ http://www.ukm.my/jsm/english_journals/vol47num6_2018/contentsVol47num6_2018.html |
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