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The effect of delays on the performance of Layer Recurrent Network

Layer Recurrent Network (LRN) is a dynamic network that has a feedback loop as well as a delay for each layer of the network except for the last layer. The main objective for this research is to study the effect of delays on the performance of the LRN in identifying a nonlinear model. A numerical ex...

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Main Authors: Li, T.C., Nordin, F.H., Yap, K.S.
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Published: 2018
Online Access:http://dspace.uniten.edu.my/jspui/handle/123456789/8920
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spelling my.uniten.dspace-89202020-09-10T07:48:35Z The effect of delays on the performance of Layer Recurrent Network Li, T.C. Nordin, F.H. Yap, K.S. Layer Recurrent Network (LRN) is a dynamic network that has a feedback loop as well as a delay for each layer of the network except for the last layer. The main objective for this research is to study the effect of delays on the performance of the LRN in identifying a nonlinear model. A numerical experiment of the nonlinear model is set up before a set of input and output data is collected. The collected data is then used to train the LRN. The numbers of delays at the feedback loop is manipulated and the effect of the network performance is observed where it shows that the network has the best performance when the number of delay is set to more than the default/original value (which is one). © 2010 IEEE. 2018-02-21T04:42:15Z 2018-02-21T04:42:15Z 2010 http://dspace.uniten.edu.my/jspui/handle/123456789/8920
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Layer Recurrent Network (LRN) is a dynamic network that has a feedback loop as well as a delay for each layer of the network except for the last layer. The main objective for this research is to study the effect of delays on the performance of the LRN in identifying a nonlinear model. A numerical experiment of the nonlinear model is set up before a set of input and output data is collected. The collected data is then used to train the LRN. The numbers of delays at the feedback loop is manipulated and the effect of the network performance is observed where it shows that the network has the best performance when the number of delay is set to more than the default/original value (which is one). © 2010 IEEE.
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author Li, T.C.
Nordin, F.H.
Yap, K.S.
spellingShingle Li, T.C.
Nordin, F.H.
Yap, K.S.
The effect of delays on the performance of Layer Recurrent Network
author_facet Li, T.C.
Nordin, F.H.
Yap, K.S.
author_sort Li, T.C.
title The effect of delays on the performance of Layer Recurrent Network
title_short The effect of delays on the performance of Layer Recurrent Network
title_full The effect of delays on the performance of Layer Recurrent Network
title_fullStr The effect of delays on the performance of Layer Recurrent Network
title_full_unstemmed The effect of delays on the performance of Layer Recurrent Network
title_sort effect of delays on the performance of layer recurrent network
publishDate 2018
url http://dspace.uniten.edu.my/jspui/handle/123456789/8920
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score 13.160551

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