Acceleration Strategies For The Backpropagation Neural Network Learning Algorithm

Algoritma perambatan balik telah terbukti sebagai salah satu algoritma rangkaian neural yang paling berjaya. Namun demikian, seperti kebanyakan kaedah pengoptimuman yang berasaskan kecerunan, ianya menumpu dengan lamb at dan keupayaannya berkurangan bagi tugas-tugas yang lebih besar dan kompleks....

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Main Author:	Zainuddin, Zarita
Format:	Thesis
Language:	English
Published:	2001
Subjects:	QA1-939 Mathematics
Online Access:	http://eprints.usm.my/31464/1/ZARITA_ZAINUDDIN.pdf
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id	my-usm-ep.31464
record_format	uketd_dc
spelling	my-usm-ep.314642017-01-06T07:49:22Z Acceleration Strategies For The Backpropagation Neural Network Learning Algorithm 2001-06 Zainuddin, Zarita QA1-939 Mathematics Algoritma perambatan balik telah terbukti sebagai salah satu algoritma rangkaian neural yang paling berjaya. Namun demikian, seperti kebanyakan kaedah pengoptimuman yang berasaskan kecerunan, ianya menumpu dengan lamb at dan keupayaannya berkurangan bagi tugas-tugas yang lebih besar dan kompleks. Dalam tesis ini, faktor-faktor yang menguasai kepantasan pembelajaran algoritma perambatan balik diselidik dan dianalisa secara matematik untuk membangunkan strategi-strategi bagi memperbaiki prestasi algoritma pembelajaran rangkaian neural ini. Faktor-faktor ini meliputi pilihan pemberat awal, pilihan fungsi pengaktifan dan nilai sasaran serta dua parameter perambatan, iaitu kadar pembelajaran dan faktor momentum. The backpropagation algorithm has proven to be one of the most successful neural network learning algorithms. However, as with many gradient based optimization methods, it converges slowly and it scales up poorly as tasks become larger and more complex. In this thesis, factors that govern the learning speed of the backpropagation algorithm are investigated and mathematically analyzed in order to develop strategies to improve the performance of this neural network learning algorithm. These factors include the choice of initial weights, the choice of activation function and target values, and the two backpropagation parameters, the learning rate and the momentum factor. 2001-06 Thesis http://eprints.usm.my/31464/ http://eprints.usm.my/31464/1/ZARITA_ZAINUDDIN.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat Pengajian Sains Matematik
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	QA1-939 Mathematics
spellingShingle	QA1-939 Mathematics Zainuddin, Zarita Acceleration Strategies For The Backpropagation Neural Network Learning Algorithm
description	Algoritma perambatan balik telah terbukti sebagai salah satu algoritma rangkaian neural yang paling berjaya. Namun demikian, seperti kebanyakan kaedah pengoptimuman yang berasaskan kecerunan, ianya menumpu dengan lamb at dan keupayaannya berkurangan bagi tugas-tugas yang lebih besar dan kompleks. Dalam tesis ini, faktor-faktor yang menguasai kepantasan pembelajaran algoritma perambatan balik diselidik dan dianalisa secara matematik untuk membangunkan strategi-strategi bagi memperbaiki prestasi algoritma pembelajaran rangkaian neural ini. Faktor-faktor ini meliputi pilihan pemberat awal, pilihan fungsi pengaktifan dan nilai sasaran serta dua parameter perambatan, iaitu kadar pembelajaran dan faktor momentum. The backpropagation algorithm has proven to be one of the most successful neural network learning algorithms. However, as with many gradient based optimization methods, it converges slowly and it scales up poorly as tasks become larger and more complex. In this thesis, factors that govern the learning speed of the backpropagation algorithm are investigated and mathematically analyzed in order to develop strategies to improve the performance of this neural network learning algorithm. These factors include the choice of initial weights, the choice of activation function and target values, and the two backpropagation parameters, the learning rate and the momentum factor.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Zainuddin, Zarita
author_facet	Zainuddin, Zarita
author_sort	Zainuddin, Zarita
title	Acceleration Strategies For The Backpropagation Neural Network Learning Algorithm
title_short	Acceleration Strategies For The Backpropagation Neural Network Learning Algorithm
title_full	Acceleration Strategies For The Backpropagation Neural Network Learning Algorithm
title_fullStr	Acceleration Strategies For The Backpropagation Neural Network Learning Algorithm
title_full_unstemmed	Acceleration Strategies For The Backpropagation Neural Network Learning Algorithm
title_sort	acceleration strategies for the backpropagation neural network learning algorithm
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Sains Matematik
publishDate	2001
url	http://eprints.usm.my/31464/1/ZARITA_ZAINUDDIN.pdf
_version_	1747820430796783616

Acceleration Strategies For The Backpropagation Neural Network Learning Algorithm

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