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Randomised Alpha-Cut Fuzzy Logic Hybrid Model in Solving 3-Satisfiability Hopfield Neural Network

This paper proposes an innovative approach to improve the performance of 3SAT logic programming in the Hopfield neural network. The merged structures of the 3SAT and Hopfield network have specific weaknesses, one of which is that, at times, the system attained local minimum solutions rather than gl...

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Main Authors: Farah Liyana, Azizan, Saratha, Sathasivam
Format: Article
Language:English
Published: Penerbit UTM Press 2023
Subjects:
QA Mathematics
Online Access:http://ir.unimas.my/id/eprint/41471/1/Randomised%20Alpha.pdf
http://ir.unimas.my/id/eprint/41471/
https://mjfas.utm.my/index.php/mjfas/article/view/2697
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spelling my.unimas.ir.414712023-03-09T03:25:42Z http://ir.unimas.my/id/eprint/41471/ Randomised Alpha-Cut Fuzzy Logic Hybrid Model in Solving 3-Satisfiability Hopfield Neural Network Farah Liyana, Azizan Saratha, Sathasivam QA Mathematics This paper proposes an innovative approach to improve the performance of 3SAT logic programming in the Hopfield neural network. The merged structures of the 3SAT and Hopfield network have specific weaknesses, one of which is that, at times, the system attained local minimum solutions rather than global minimum solutions. A new model of integration randomised alpha-cut fuzzy logic with 3SAT in the Hopfield network is built to convey information more effectively. 3SAT and fuzzy logic can work together to solve Hopfield networks' combinatorial optimisation issues. Procedures of fuzzifying and defuzzifying the neurons might ease the computational burden of determining the correct neuron states. Until the proper neuron state is established, unsatisfied neuron clauses will be modified using a randomised alpha-cut approach in the defuzzifier step. An incorporated design built a random approach to select the alpha-cut values of 0.1, 0.25, and 0.5. At this point, a fuzzy value switches into a crisp output back through the defuzzifier process. Based on the results obtained, the proposed hybrid strategy effectively improves the indicators of RMSE, SSE, MAE, MAPE, global minima and total computational time. A computer-generated data set was used to measure how well the hybridised techniques performed. The performance of the proposed network was trained and validated using Matlab 2020b. The results are significant because this model significantly affects how successfully Hopfield networks merged with fuzzy logic can tackle the 3SAT challenges. The obtained data and ideas will help to create novel approaches to data collection for upcoming logic programming exploration. Penerbit UTM Press 2023 Article PeerReviewed text en http://ir.unimas.my/id/eprint/41471/1/Randomised%20Alpha.pdf Farah Liyana, Azizan and Saratha, Sathasivam (2023) Randomised Alpha-Cut Fuzzy Logic Hybrid Model in Solving 3-Satisfiability Hopfield Neural Network. Malaysian Journal of Fundamental and Applied Sciences, 19 (1). pp. 43-55. ISSN 2289-599X https://mjfas.utm.my/index.php/mjfas/article/view/2697 DOI: https://doi.org/10.11113/mjfas.v19n1.2697
institution Universiti Malaysia Sarawak
building Centre for Academic Information Services (CAIS)
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sarawak
content_source UNIMAS Institutional Repository
url_provider http://ir.unimas.my/
language English
topic QA Mathematics
spellingShingle QA Mathematics
Farah Liyana, Azizan
Saratha, Sathasivam
Randomised Alpha-Cut Fuzzy Logic Hybrid Model in Solving 3-Satisfiability Hopfield Neural Network
description This paper proposes an innovative approach to improve the performance of 3SAT logic programming in the Hopfield neural network. The merged structures of the 3SAT and Hopfield network have specific weaknesses, one of which is that, at times, the system attained local minimum solutions rather than global minimum solutions. A new model of integration randomised alpha-cut fuzzy logic with 3SAT in the Hopfield network is built to convey information more effectively. 3SAT and fuzzy logic can work together to solve Hopfield networks' combinatorial optimisation issues. Procedures of fuzzifying and defuzzifying the neurons might ease the computational burden of determining the correct neuron states. Until the proper neuron state is established, unsatisfied neuron clauses will be modified using a randomised alpha-cut approach in the defuzzifier step. An incorporated design built a random approach to select the alpha-cut values of 0.1, 0.25, and 0.5. At this point, a fuzzy value switches into a crisp output back through the defuzzifier process. Based on the results obtained, the proposed hybrid strategy effectively improves the indicators of RMSE, SSE, MAE, MAPE, global minima and total computational time. A computer-generated data set was used to measure how well the hybridised techniques performed. The performance of the proposed network was trained and validated using Matlab 2020b. The results are significant because this model significantly affects how successfully Hopfield networks merged with fuzzy logic can tackle the 3SAT challenges. The obtained data and ideas will help to create novel approaches to data collection for upcoming logic programming exploration.
format Article
author Farah Liyana, Azizan
Saratha, Sathasivam
author_facet Farah Liyana, Azizan
Saratha, Sathasivam
author_sort Farah Liyana, Azizan
title Randomised Alpha-Cut Fuzzy Logic Hybrid Model in Solving 3-Satisfiability Hopfield Neural Network
title_short Randomised Alpha-Cut Fuzzy Logic Hybrid Model in Solving 3-Satisfiability Hopfield Neural Network
title_full Randomised Alpha-Cut Fuzzy Logic Hybrid Model in Solving 3-Satisfiability Hopfield Neural Network
title_fullStr Randomised Alpha-Cut Fuzzy Logic Hybrid Model in Solving 3-Satisfiability Hopfield Neural Network
title_full_unstemmed Randomised Alpha-Cut Fuzzy Logic Hybrid Model in Solving 3-Satisfiability Hopfield Neural Network
title_sort randomised alpha-cut fuzzy logic hybrid model in solving 3-satisfiability hopfield neural network
publisher Penerbit UTM Press
publishDate 2023
url http://ir.unimas.my/id/eprint/41471/1/Randomised%20Alpha.pdf
http://ir.unimas.my/id/eprint/41471/
https://mjfas.utm.my/index.php/mjfas/article/view/2697
_version_ 1761623546387234816
score 13.244404

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