A hybrid BP and HSA for enhancing a multilayer perceptron learning

Training neural networks is a great significance of a difficult task in the field of supervised learning because; its performance depends on underlying training algorithm as well as the achievement of the training process. In this study, three training algorithms namely Back-Propagation algorithm, H...

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Main Author:	Salad Nur, Abdirashid
Format:	Thesis
Published:	2014
Subjects:	LB Theory and practice of education
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id	my-utm-ep.48316
record_format	uketd_dc
spelling	my-utm-ep.483162017-07-26T04:36:13Z A hybrid BP and HSA for enhancing a multilayer perceptron learning 2014 Salad Nur, Abdirashid LB Theory and practice of education Training neural networks is a great significance of a difficult task in the field of supervised learning because; its performance depends on underlying training algorithm as well as the achievement of the training process. In this study, three training algorithms namely Back-Propagation algorithm, Harmony Search Algorithm (HSA) and hybrid BP and HSA called BPHSA are employed for the supervised training of MLP feed forward type of NNs by giving special attention to hybrid BPHSA. A suitable structure for data representation of NNs is implemented to BPHSA, HSA and BP. The proposed model is empirically tested and verified by using five benchmark classification problems namely Iris, Glass, Cancer, Wine and thyroid datasets on training NNs. The MSE, training time, classification accuracy of hybrid BPHSA are compared with the standard BP and meta-heuristic HSA. The experiments showed that proposed model (BPHSA) has better results in terms of convergence error and classification accuracy compared to BP and HSA and this makes the BPHSA look as promising algorithm for neural network training 2014 Thesis http://eprints.utm.my/id/eprint/48316/ masters Universiti Teknologi Malaysia, Faculty of Computing Faculty of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
topic	LB Theory and practice of education
spellingShingle	LB Theory and practice of education Salad Nur, Abdirashid A hybrid BP and HSA for enhancing a multilayer perceptron learning
description	Training neural networks is a great significance of a difficult task in the field of supervised learning because; its performance depends on underlying training algorithm as well as the achievement of the training process. In this study, three training algorithms namely Back-Propagation algorithm, Harmony Search Algorithm (HSA) and hybrid BP and HSA called BPHSA are employed for the supervised training of MLP feed forward type of NNs by giving special attention to hybrid BPHSA. A suitable structure for data representation of NNs is implemented to BPHSA, HSA and BP. The proposed model is empirically tested and verified by using five benchmark classification problems namely Iris, Glass, Cancer, Wine and thyroid datasets on training NNs. The MSE, training time, classification accuracy of hybrid BPHSA are compared with the standard BP and meta-heuristic HSA. The experiments showed that proposed model (BPHSA) has better results in terms of convergence error and classification accuracy compared to BP and HSA and this makes the BPHSA look as promising algorithm for neural network training
format	Thesis
qualification_level	Master's degree
author	Salad Nur, Abdirashid
author_facet	Salad Nur, Abdirashid
author_sort	Salad Nur, Abdirashid
title	A hybrid BP and HSA for enhancing a multilayer perceptron learning
title_short	A hybrid BP and HSA for enhancing a multilayer perceptron learning
title_full	A hybrid BP and HSA for enhancing a multilayer perceptron learning
title_fullStr	A hybrid BP and HSA for enhancing a multilayer perceptron learning
title_full_unstemmed	A hybrid BP and HSA for enhancing a multilayer perceptron learning
title_sort	hybrid bp and hsa for enhancing a multilayer perceptron learning
granting_institution	Universiti Teknologi Malaysia, Faculty of Computing
granting_department	Faculty of Computing
publishDate	2014
_version_	1747817360728784896

A hybrid BP and HSA for enhancing a multilayer perceptron learning

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