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Dynamic training rate for backpropagation learning algorithm

In this paper, we created a dynamic function training rate for the Back propagation learning algorithm to avoid the local minimum and to speed up training.The Back propagation with dynamic training rate (BPDR) algorithm uses the sigmoid function.The 2-dimensional XOR problem and iris data were used...

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Main Authors: Al-Duais, M. S., Yaakub, Abdul Razak, Yusoff, Nooraini
Format: Conference or Workshop Item
Language:English
Published: 2013
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://repo.uum.edu.my/19123/1/MICC%202013%20277-282.pdf
http://repo.uum.edu.my/19123/
http://doi.org/10.1109/MICC.2013.6805839
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spelling my.uum.repo.191232016-11-10T03:29:55Z http://repo.uum.edu.my/19123/ Dynamic training rate for backpropagation learning algorithm Al-Duais, M. S. Yaakub, Abdul Razak Yusoff, Nooraini QA75 Electronic computers. Computer science In this paper, we created a dynamic function training rate for the Back propagation learning algorithm to avoid the local minimum and to speed up training.The Back propagation with dynamic training rate (BPDR) algorithm uses the sigmoid function.The 2-dimensional XOR problem and iris data were used as benchmarks to test the effects of the dynamic training rate formulated in this paper.The results of these experiments demonstrate that the BPDR algorithm is advantageous with regards to both generalization performance and training speed. The stop training or limited error was determined by1.0 e-5 2013-11-26 Conference or Workshop Item PeerReviewed application/pdf en http://repo.uum.edu.my/19123/1/MICC%202013%20277-282.pdf Al-Duais, M. S. and Yaakub, Abdul Razak and Yusoff, Nooraini (2013) Dynamic training rate for backpropagation learning algorithm. In: IEEE 11th Malaysia International Conference on Communications (MICC), 26-28 Nov. 2013, Kuala Lumpur, Malaysia. http://doi.org/10.1109/MICC.2013.6805839 doi:10.1109/MICC.2013.6805839
institution Universiti Utara Malaysia
building UUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Utara Malaysia
content_source UUM Institutionali Repository
url_provider http://repo.uum.edu.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Al-Duais, M. S.
Yaakub, Abdul Razak
Yusoff, Nooraini
Dynamic training rate for backpropagation learning algorithm
description In this paper, we created a dynamic function training rate for the Back propagation learning algorithm to avoid the local minimum and to speed up training.The Back propagation with dynamic training rate (BPDR) algorithm uses the sigmoid function.The 2-dimensional XOR problem and iris data were used as benchmarks to test the effects of the dynamic training rate formulated in this paper.The results of these experiments demonstrate that the BPDR algorithm is advantageous with regards to both generalization performance and training speed. The stop training or limited error was determined by1.0 e-5
format Conference or Workshop Item
author Al-Duais, M. S.
Yaakub, Abdul Razak
Yusoff, Nooraini
author_facet Al-Duais, M. S.
Yaakub, Abdul Razak
Yusoff, Nooraini
author_sort Al-Duais, M. S.
title Dynamic training rate for backpropagation learning algorithm
title_short Dynamic training rate for backpropagation learning algorithm
title_full Dynamic training rate for backpropagation learning algorithm
title_fullStr Dynamic training rate for backpropagation learning algorithm
title_full_unstemmed Dynamic training rate for backpropagation learning algorithm
title_sort dynamic training rate for backpropagation learning algorithm
publishDate 2013
url http://repo.uum.edu.my/19123/1/MICC%202013%20277-282.pdf
http://repo.uum.edu.my/19123/
http://doi.org/10.1109/MICC.2013.6805839
_version_ 1644282623563923456
score 13.154949

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