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Fast and efficient sequential learning algorithms using direct-link RBF networks

Novel fast and efficient sequential learning algorithms are proposed for direct-link radial basis function (DRBF) networks. The dynamic DRBF network is trained using the recently proposed decomposed/parallel recursive Levenberg Marquardt (PRLM) algorithm by neglecting the interneuron weight interact...

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Main Authors: Asirvadam , Vijanth Sagayan, McLoone, Sean, Irwin, George
Other Authors: Christophe , Molina
Format: Book Section
Published: IEEE Press 2003
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://isp.imm.dtu.dk/nnsp2003
http://eprints.utp.edu.my/3828/
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id my.utp.eprints.3828
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spelling my.utp.eprints.38282011-01-04T00:42:19Z Fast and efficient sequential learning algorithms using direct-link RBF networks Asirvadam , Vijanth Sagayan McLoone, Sean Irwin, George TK Electrical engineering. Electronics Nuclear engineering Novel fast and efficient sequential learning algorithms are proposed for direct-link radial basis function (DRBF) networks. The dynamic DRBF network is trained using the recently proposed decomposed/parallel recursive Levenberg Marquardt (PRLM) algorithm by neglecting the interneuron weight interactions. The resulting sequential learning approach enables weights to be updated in an efficient parallel manner and facilitates a minimal update extension for real-time applications. Simulation results for two benchmark problems show the feasibility of the new training algorithms. IEEE Press Christophe , Molina Tülay , Adali Jan , Larsen Marc , Van Hulle 2003 Book Section PeerReviewed http://isp.imm.dtu.dk/nnsp2003 Asirvadam , Vijanth Sagayan and McLoone, Sean and Irwin, George (2003) Fast and efficient sequential learning algorithms using direct-link RBF networks. In: Neural Networks For Signal Processing XIII. NEURAL NETWORKS For SIGNAL PROCESSING (XIII). IEEE Press, Piscataway, New Jersey, pp. 209-218. ISBN 0-7803-8178-5 http://eprints.utp.edu.my/3828/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Asirvadam , Vijanth Sagayan
McLoone, Sean
Irwin, George
Fast and efficient sequential learning algorithms using direct-link RBF networks
description Novel fast and efficient sequential learning algorithms are proposed for direct-link radial basis function (DRBF) networks. The dynamic DRBF network is trained using the recently proposed decomposed/parallel recursive Levenberg Marquardt (PRLM) algorithm by neglecting the interneuron weight interactions. The resulting sequential learning approach enables weights to be updated in an efficient parallel manner and facilitates a minimal update extension for real-time applications. Simulation results for two benchmark problems show the feasibility of the new training algorithms.
author2 Christophe , Molina
author_facet Christophe , Molina
Asirvadam , Vijanth Sagayan
McLoone, Sean
Irwin, George
format Book Section
author Asirvadam , Vijanth Sagayan
McLoone, Sean
Irwin, George
author_sort Asirvadam , Vijanth Sagayan
title Fast and efficient sequential learning algorithms using direct-link RBF networks
title_short Fast and efficient sequential learning algorithms using direct-link RBF networks
title_full Fast and efficient sequential learning algorithms using direct-link RBF networks
title_fullStr Fast and efficient sequential learning algorithms using direct-link RBF networks
title_full_unstemmed Fast and efficient sequential learning algorithms using direct-link RBF networks
title_sort fast and efficient sequential learning algorithms using direct-link rbf networks
publisher IEEE Press
publishDate 2003
url http://isp.imm.dtu.dk/nnsp2003
http://eprints.utp.edu.my/3828/
_version_ 1738655299382804480
score 13.19449

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