Adapting and enhancing mussels wandering optimization algorithm for supervised training of neural networks

Membangunkan kaedah latihan yang cekap untuk Rangkaian Neural (NN) dalam mencapai kejituan yang tinggi adalah satu cabaran. Tambahan pula, latihan NN masih lagi memerlukan masa yang lama. Algoritma Pengoptimuman Perayauan Kupang (MWO) ialah satu algoritma pengoptimuman metaheuristik yang baru dan...

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Main Author:	Abusnaina, Ahmed A. A.
Format:	Thesis
Language:	English
Published:	2015
Subjects:	QA75.5-76.95 Electronic computers Computer science
Online Access:	http://eprints.usm.my/35580/1/PhD_Thesis_Abusnaina_Adapting_and__Enhancing_MWO_Algorithm_for_Supervised_Trainnig_of_NN_%281%29.pdf
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spelling	my-usm-ep.355802019-04-12T05:25:44Z Adapting and enhancing mussels wandering optimization algorithm for supervised training of neural networks 2015-04 Abusnaina, Ahmed A. A. QA75.5-76.95 Electronic computers. Computer science Membangunkan kaedah latihan yang cekap untuk Rangkaian Neural (NN) dalam mencapai kejituan yang tinggi adalah satu cabaran. Tambahan pula, latihan NN masih lagi memerlukan masa yang lama. Algoritma Pengoptimuman Perayauan Kupang (MWO) ialah satu algoritma pengoptimuman metaheuristik yang baru dan telah diinspirasikan secara ekologi oleh tingkah laku pegerakan kupang. Objektif utama bagi tesis ini adalah untuk mencapai prestasi yang terbaik dalam penumpuan masa latihan dan ketepatan pengelasan untuk pengelasan corak dengan mengusulkan kaedah latihan penyeliaan yang baru untuk Rangkaian Neural Buatan (ANN) yang berasaskan penggunaan algoritma MWO. Mempertingkatkan prestasi, terutamanya dalam kejituan pengelasan yang membawa kepada perkenalan versi MWO yang telah di adaptasi; dikenali sebagai algoritma Peningkatan-MWO (E-MWO). Developing efficient training method for Neural Networks (NN) in terms of high accuracy is a challenge. In addition, training NN is still highly-time consuming. The Mussels Wandering Optimization (MWO) is a recent metaheuristic optimization algorithm inspired ecologically by mussels movement behavior. The major objective of this thesis is to achieve better performance in terms of convergence training time and classification accuracy for pattern classification by proposing new supervised training methods for Artificial Neural Networks (ANN) based on the MWO algorithm. Increasing the performance, especially in terms of classification accuracy led to an adapted version of the MWO; known as Enhanced-MWO (E-MWO) algorithm. 2015-04 Thesis http://eprints.usm.my/35580/ http://eprints.usm.my/35580/1/PhD_Thesis_Abusnaina_Adapting_and__Enhancing_MWO_Algorithm_for_Supervised_Trainnig_of_NN_%281%29.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat Pengajian Sains Komputer (School of Computer Sciences)
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	QA75.5-76.95 Electronic computers Computer science
spellingShingle	QA75.5-76.95 Electronic computers Computer science Abusnaina, Ahmed A. A. Adapting and enhancing mussels wandering optimization algorithm for supervised training of neural networks
description	Membangunkan kaedah latihan yang cekap untuk Rangkaian Neural (NN) dalam mencapai kejituan yang tinggi adalah satu cabaran. Tambahan pula, latihan NN masih lagi memerlukan masa yang lama. Algoritma Pengoptimuman Perayauan Kupang (MWO) ialah satu algoritma pengoptimuman metaheuristik yang baru dan telah diinspirasikan secara ekologi oleh tingkah laku pegerakan kupang. Objektif utama bagi tesis ini adalah untuk mencapai prestasi yang terbaik dalam penumpuan masa latihan dan ketepatan pengelasan untuk pengelasan corak dengan mengusulkan kaedah latihan penyeliaan yang baru untuk Rangkaian Neural Buatan (ANN) yang berasaskan penggunaan algoritma MWO. Mempertingkatkan prestasi, terutamanya dalam kejituan pengelasan yang membawa kepada perkenalan versi MWO yang telah di adaptasi; dikenali sebagai algoritma Peningkatan-MWO (E-MWO). Developing efficient training method for Neural Networks (NN) in terms of high accuracy is a challenge. In addition, training NN is still highly-time consuming. The Mussels Wandering Optimization (MWO) is a recent metaheuristic optimization algorithm inspired ecologically by mussels movement behavior. The major objective of this thesis is to achieve better performance in terms of convergence training time and classification accuracy for pattern classification by proposing new supervised training methods for Artificial Neural Networks (ANN) based on the MWO algorithm. Increasing the performance, especially in terms of classification accuracy led to an adapted version of the MWO; known as Enhanced-MWO (E-MWO) algorithm.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Abusnaina, Ahmed A. A.
author_facet	Abusnaina, Ahmed A. A.
author_sort	Abusnaina, Ahmed A. A.
title	Adapting and enhancing mussels wandering optimization algorithm for supervised training of neural networks
title_short	Adapting and enhancing mussels wandering optimization algorithm for supervised training of neural networks
title_full	Adapting and enhancing mussels wandering optimization algorithm for supervised training of neural networks
title_fullStr	Adapting and enhancing mussels wandering optimization algorithm for supervised training of neural networks
title_full_unstemmed	Adapting and enhancing mussels wandering optimization algorithm for supervised training of neural networks
title_sort	adapting and enhancing mussels wandering optimization algorithm for supervised training of neural networks
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Sains Komputer (School of Computer Sciences)
publishDate	2015
url	http://eprints.usm.my/35580/1/PhD_Thesis_Abusnaina_Adapting_and__Enhancing_MWO_Algorithm_for_Supervised_Trainnig_of_NN_%281%29.pdf
_version_	1747820595358203904

Adapting and enhancing mussels wandering optimization algorithm for supervised training of neural networks

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