Study Of Modified Training Algorithm For Optimized Convergence Speed Of Neural Network

Artificial neural network (ANN) are widely used as an engineering approach to mimic the human brain activities. It has applied in different aspects such as pattern recognition, alphabet or digit classification, handwriting recognition, speech recognition, fingerprint identification, data mining, rob...

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Main Author:	Kang, Miew How
Format:	Thesis
Language:	English
Published:	2016
Subjects:	TK7800-8360 Electronics
Online Access:	http://eprints.usm.my/41299/1/KANG_MIEW_HOW_24_Pages.pdf
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id	my-usm-ep.41299
record_format	uketd_dc
spelling	my-usm-ep.412992018-08-13T06:54:12Z Study Of Modified Training Algorithm For Optimized Convergence Speed Of Neural Network 2016 Kang, Miew How TK7800-8360 Electronics Artificial neural network (ANN) are widely used as an engineering approach to mimic the human brain activities. It has applied in different aspects such as pattern recognition, alphabet or digit classification, handwriting recognition, speech recognition, fingerprint identification, data mining, robots and etc. Back-propagation is the most common artificial neural training algorithm, however it is suffering with the slow convergence rate issue. A study to improve the slow convergence rate of a neural network without sacrificing the accuracy of the network are carried out. In this research, a hand-written character recognition model are implemented in C++ programming with ability to classify digits 0, 1, 2, and 3. This model are built up with 64-40-4 neural network where input data are 8 x 8 dimension image and output are classified to 4 digits which are 0, 1, 2 and 3. Two modified algorithms are proposed in this research, which are mixture of the momentum algorithm with different learning rate algorithms. First proposed algorithm is the combination of momentum algorithm with adaptive learning rate (ALR) algorithm, and second proposed algorithm is the combination of momentum algorithm with automatic learning rate selection (ALRS) algorithm. Convergence rate are showed 18% improvement in the algorithm mixture with ALR algorithm, however there is no significant improvement of timing for algorithm mixture with ALRS algorithm. 2016 Thesis http://eprints.usm.my/41299/ http://eprints.usm.my/41299/1/KANG_MIEW_HOW_24_Pages.pdf application/pdf en public masters Universiti Sains Malaysia Pusat Pengajian Kejuruteraan Elektrik dan Elektronik
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	TK7800-8360 Electronics
spellingShingle	TK7800-8360 Electronics Kang, Miew How Study Of Modified Training Algorithm For Optimized Convergence Speed Of Neural Network
description	Artificial neural network (ANN) are widely used as an engineering approach to mimic the human brain activities. It has applied in different aspects such as pattern recognition, alphabet or digit classification, handwriting recognition, speech recognition, fingerprint identification, data mining, robots and etc. Back-propagation is the most common artificial neural training algorithm, however it is suffering with the slow convergence rate issue. A study to improve the slow convergence rate of a neural network without sacrificing the accuracy of the network are carried out. In this research, a hand-written character recognition model are implemented in C++ programming with ability to classify digits 0, 1, 2, and 3. This model are built up with 64-40-4 neural network where input data are 8 x 8 dimension image and output are classified to 4 digits which are 0, 1, 2 and 3. Two modified algorithms are proposed in this research, which are mixture of the momentum algorithm with different learning rate algorithms. First proposed algorithm is the combination of momentum algorithm with adaptive learning rate (ALR) algorithm, and second proposed algorithm is the combination of momentum algorithm with automatic learning rate selection (ALRS) algorithm. Convergence rate are showed 18% improvement in the algorithm mixture with ALR algorithm, however there is no significant improvement of timing for algorithm mixture with ALRS algorithm.
format	Thesis
qualification_level	Master's degree
author	Kang, Miew How
author_facet	Kang, Miew How
author_sort	Kang, Miew How
title	Study Of Modified Training Algorithm For Optimized Convergence Speed Of Neural Network
title_short	Study Of Modified Training Algorithm For Optimized Convergence Speed Of Neural Network
title_full	Study Of Modified Training Algorithm For Optimized Convergence Speed Of Neural Network
title_fullStr	Study Of Modified Training Algorithm For Optimized Convergence Speed Of Neural Network
title_full_unstemmed	Study Of Modified Training Algorithm For Optimized Convergence Speed Of Neural Network
title_sort	study of modified training algorithm for optimized convergence speed of neural network
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Kejuruteraan Elektrik dan Elektronik
publishDate	2016
url	http://eprints.usm.my/41299/1/KANG_MIEW_HOW_24_Pages.pdf
_version_	1747820905238626304

Study Of Modified Training Algorithm For Optimized Convergence Speed Of Neural Network

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