Binary Artificial Bee Colony Optimization For Weighted Random 2 Satisfiability In Discrete Hopfield Neural Network

One of the alternatives to improve the modeling of the Discrete Hopfield Neural Network is by implementing different variants of logical rules. In this context, Satisfiability is suitable as a logical rule in Discrete Hopfield Neural Network due to the simplicity of the structure, and fault toleranc...

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Main Author:	Muhammad Sidik, Siti Syatirah
Format:	Thesis
Language:	English
Published:	2023
Subjects:	QA1 Mathematics (General)
Online Access:	http://eprints.usm.my/60482/1/24%20Pages%20from%20SITI%20SYATIRAH%20BINTI%20MUHAMMAD%20SIDIK.pdf
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id	my-usm-ep.60482
record_format	uketd_dc
spelling	my-usm-ep.604822024-04-30T02:57:56Z Binary Artificial Bee Colony Optimization For Weighted Random 2 Satisfiability In Discrete Hopfield Neural Network 2023-05 Muhammad Sidik, Siti Syatirah QA1 Mathematics (General) One of the alternatives to improve the modeling of the Discrete Hopfield Neural Network is by implementing different variants of logical rules. In this context, Satisfiability is suitable as a logical rule in Discrete Hopfield Neural Network due to the simplicity of the structure, and fault tolerance. Hence, this thesis will utilize Non-Systematic Weighted Random 2 Satisfiability incorporating with Binary Artificial Bee Colony algorithm in Discrete Hopfield Neural Network. The Binary Artificial Bee Colony will be utilized to optimize the logical structure according to the ratio of negative literals by capitalizing the features of the exploration mechanism of the algorithm. Then, the Election algorithm will be utilized to obtain a satisfied interpretation of the correct logical structure in the training phase of the Discrete Hopfield Neural Network. This proposed model will be employed in the Improved Reverse Analysis method to extract the relationship between various fields of real-life data sets based on logical representation. This thesis will be presented by implementing simulated, and benchmark data sets with multiple performance evaluation metrics. Based on the findings, the proposed model outperforms other models. 2023-05 Thesis http://eprints.usm.my/60482/ http://eprints.usm.my/60482/1/24%20Pages%20from%20SITI%20SYATIRAH%20BINTI%20MUHAMMAD%20SIDIK.pdf application/pdf en public masters Perpustakaan Hamzah Sendut Pusat Pengajian Sains Matematik
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	QA1 Mathematics (General)
spellingShingle	QA1 Mathematics (General) Muhammad Sidik, Siti Syatirah Binary Artificial Bee Colony Optimization For Weighted Random 2 Satisfiability In Discrete Hopfield Neural Network
description	One of the alternatives to improve the modeling of the Discrete Hopfield Neural Network is by implementing different variants of logical rules. In this context, Satisfiability is suitable as a logical rule in Discrete Hopfield Neural Network due to the simplicity of the structure, and fault tolerance. Hence, this thesis will utilize Non-Systematic Weighted Random 2 Satisfiability incorporating with Binary Artificial Bee Colony algorithm in Discrete Hopfield Neural Network. The Binary Artificial Bee Colony will be utilized to optimize the logical structure according to the ratio of negative literals by capitalizing the features of the exploration mechanism of the algorithm. Then, the Election algorithm will be utilized to obtain a satisfied interpretation of the correct logical structure in the training phase of the Discrete Hopfield Neural Network. This proposed model will be employed in the Improved Reverse Analysis method to extract the relationship between various fields of real-life data sets based on logical representation. This thesis will be presented by implementing simulated, and benchmark data sets with multiple performance evaluation metrics. Based on the findings, the proposed model outperforms other models.
format	Thesis
qualification_level	Master's degree
author	Muhammad Sidik, Siti Syatirah
author_facet	Muhammad Sidik, Siti Syatirah
author_sort	Muhammad Sidik, Siti Syatirah
title	Binary Artificial Bee Colony Optimization For Weighted Random 2 Satisfiability In Discrete Hopfield Neural Network
title_short	Binary Artificial Bee Colony Optimization For Weighted Random 2 Satisfiability In Discrete Hopfield Neural Network
title_full	Binary Artificial Bee Colony Optimization For Weighted Random 2 Satisfiability In Discrete Hopfield Neural Network
title_fullStr	Binary Artificial Bee Colony Optimization For Weighted Random 2 Satisfiability In Discrete Hopfield Neural Network
title_full_unstemmed	Binary Artificial Bee Colony Optimization For Weighted Random 2 Satisfiability In Discrete Hopfield Neural Network
title_sort	binary artificial bee colony optimization for weighted random 2 satisfiability in discrete hopfield neural network
granting_institution	Perpustakaan Hamzah Sendut
granting_department	Pusat Pengajian Sains Matematik
publishDate	2023
url	http://eprints.usm.my/60482/1/24%20Pages%20from%20SITI%20SYATIRAH%20BINTI%20MUHAMMAD%20SIDIK.pdf
_version_	1804888945881776128

Binary Artificial Bee Colony Optimization For Weighted Random 2 Satisfiability In Discrete Hopfield Neural Network

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