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An improved back propagation leaning algorithm using second order methods with gain parameter

Back Propagation (BP) algorithm is one of the oldest learning techniques used by Artificial Neural Networks (ANN). It has successfully been implemented in various practical problems. However, the algorithm still faces some drawbacks such as getting easily stuck at local minima and needs longer time...

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Main Authors:	Mohd Nawi, Nazri, Mohamed Saufi, Noor Haliza, Budiyono, Avon, Abdul Hamid, Noorhamreeza, Rehman Gillani, Syed Muhammad Zubair, Ramli, Azizul Azhar
Format:	Article
Language:	English
Published:	Penerbit UTHM 2018
Subjects:	TA168 Systems engineering TS155-194 Production management. Operations management
Online Access:	http://eprints.uthm.edu.my/4558/1/AJ%202018%20%28791%29%20An%20improved%20back%20propagation%20leaning%20algorithm%20using%20second%20order%20methods%20with%20gain%20parameter.pdf http://eprints.uthm.edu.my/4558/
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id	my.uthm.eprints.4558
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spelling	my.uthm.eprints.45582021-12-07T07:17:49Z http://eprints.uthm.edu.my/4558/ An improved back propagation leaning algorithm using second order methods with gain parameter Mohd Nawi, Nazri Mohamed Saufi, Noor Haliza Budiyono, Avon Abdul Hamid, Noorhamreeza Rehman Gillani, Syed Muhammad Zubair Ramli, Azizul Azhar TA168 Systems engineering TS155-194 Production management. Operations management Back Propagation (BP) algorithm is one of the oldest learning techniques used by Artificial Neural Networks (ANN). It has successfully been implemented in various practical problems. However, the algorithm still faces some drawbacks such as getting easily stuck at local minima and needs longer time to converge on an acceptable solution. Recently, the introduction of Second Order Methods has shown a significant improvement on the learning in BP but it still has some drawbacks such as slow convergence and complexity. To overcome these limitations, this research proposed a modified approach for BP by introducing the Conjugate Gradient and QuasiNewton which were Second Order methods together with ‘gain’ parameter. The performances of the proposed approach is evaluated in terms of lowest number of epochs, lowest CPU time and highest accuracy on five benchmark classification datasets such as Glass, Horse, 7Bit Parity, Indian Liver Patient and Lung Cancer. The results show that the proposed Second Order methods with ‘gain’ performed better than the BP algorithm. Penerbit UTHM 2018 Article PeerReviewed text en http://eprints.uthm.edu.my/4558/1/AJ%202018%20%28791%29%20An%20improved%20back%20propagation%20leaning%20algorithm%20using%20second%20order%20methods%20with%20gain%20parameter.pdf Mohd Nawi, Nazri and Mohamed Saufi, Noor Haliza and Budiyono, Avon and Abdul Hamid, Noorhamreeza and Rehman Gillani, Syed Muhammad Zubair and Ramli, Azizul Azhar (2018) An improved back propagation leaning algorithm using second order methods with gain parameter. International Journal of Integrated Engineering, 10 (6). pp. 11-18. ISSN 2229-838X
institution	Universiti Tun Hussein Onn Malaysia
building	UTHM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tun Hussein Onn Malaysia
content_source	UTHM Institutional Repository
url_provider	http://eprints.uthm.edu.my/
language	English
topic	TA168 Systems engineering TS155-194 Production management. Operations management
spellingShingle	TA168 Systems engineering TS155-194 Production management. Operations management Mohd Nawi, Nazri Mohamed Saufi, Noor Haliza Budiyono, Avon Abdul Hamid, Noorhamreeza Rehman Gillani, Syed Muhammad Zubair Ramli, Azizul Azhar An improved back propagation leaning algorithm using second order methods with gain parameter
description	Back Propagation (BP) algorithm is one of the oldest learning techniques used by Artificial Neural Networks (ANN). It has successfully been implemented in various practical problems. However, the algorithm still faces some drawbacks such as getting easily stuck at local minima and needs longer time to converge on an acceptable solution. Recently, the introduction of Second Order Methods has shown a significant improvement on the learning in BP but it still has some drawbacks such as slow convergence and complexity. To overcome these limitations, this research proposed a modified approach for BP by introducing the Conjugate Gradient and QuasiNewton which were Second Order methods together with ‘gain’ parameter. The performances of the proposed approach is evaluated in terms of lowest number of epochs, lowest CPU time and highest accuracy on five benchmark classification datasets such as Glass, Horse, 7Bit Parity, Indian Liver Patient and Lung Cancer. The results show that the proposed Second Order methods with ‘gain’ performed better than the BP algorithm.
format	Article
author	Mohd Nawi, Nazri Mohamed Saufi, Noor Haliza Budiyono, Avon Abdul Hamid, Noorhamreeza Rehman Gillani, Syed Muhammad Zubair Ramli, Azizul Azhar
author_facet	Mohd Nawi, Nazri Mohamed Saufi, Noor Haliza Budiyono, Avon Abdul Hamid, Noorhamreeza Rehman Gillani, Syed Muhammad Zubair Ramli, Azizul Azhar
author_sort	Mohd Nawi, Nazri
title	An improved back propagation leaning algorithm using second order methods with gain parameter
title_short	An improved back propagation leaning algorithm using second order methods with gain parameter
title_full	An improved back propagation leaning algorithm using second order methods with gain parameter
title_fullStr	An improved back propagation leaning algorithm using second order methods with gain parameter
title_full_unstemmed	An improved back propagation leaning algorithm using second order methods with gain parameter
title_sort	improved back propagation leaning algorithm using second order methods with gain parameter
publisher	Penerbit UTHM
publishDate	2018
url	http://eprints.uthm.edu.my/4558/1/AJ%202018%20%28791%29%20An%20improved%20back%20propagation%20leaning%20algorithm%20using%20second%20order%20methods%20with%20gain%20parameter.pdf http://eprints.uthm.edu.my/4558/
_version_	1738581266195808256
score	13.211869

An improved back propagation leaning algorithm using second order methods with gain parameter

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