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An active learning approach for radial basis function neural networks

This paper presents a new Active Learning algorithm to train Radial Basis Function (RBF) Artificial Neural Networks (ANN) for model reduction problems. The new approach is based on the assumption that the unobserved training data y at input x, lies within a set F x y f x y f x ( ) : ( ) ( ) = ! ! &q...

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Main Authors: Abdullah, S. S., Allwright, J. C.
Format: Article
Language:English
Published: Penerbit UTM Press 2006
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://eprints.utm.my/id/eprint/4112/1/JTD_2005_29.pdf
http://eprints.utm.my/id/eprint/4112/
http://www.penerbit.utm.my/onlinejournal/45/D/JTDis45D05.pdf
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id my.utm.4112
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spelling my.utm.41122017-11-01T04:17:28Z http://eprints.utm.my/id/eprint/4112/ An active learning approach for radial basis function neural networks Abdullah, S. S. Allwright, J. C. TK Electrical engineering. Electronics Nuclear engineering This paper presents a new Active Learning algorithm to train Radial Basis Function (RBF) Artificial Neural Networks (ANN) for model reduction problems. The new approach is based on the assumption that the unobserved training data y at input x, lies within a set F x y f x y f x ( ) : ( ) ( ) = ! ! " # where F(x) is known from experience or past simulations. The new approach finds the location of the new sample such that the worst case error between the output of the resulting RBF ANN and the bounds of the unknown data as specified by F(x) is minimized. This paper illustrates the new approach for the case when x " R1. It was found that it is possible to find a good location for the new data sample by using the suggested approach in certain cases. A comparative study was also done indicating that the new experiment design approach is a good complement to the existing ones such as cross validation design and maximum minimum design. Penerbit UTM Press 2006-12 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/4112/1/JTD_2005_29.pdf Abdullah, S. S. and Allwright, J. C. (2006) An active learning approach for radial basis function neural networks. Jurnal Teknologi D (45D). pp. 77-96. ISSN 0127-9696 http://www.penerbit.utm.my/onlinejournal/45/D/JTDis45D05.pdf
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Abdullah, S. S.
Allwright, J. C.
An active learning approach for radial basis function neural networks
description This paper presents a new Active Learning algorithm to train Radial Basis Function (RBF) Artificial Neural Networks (ANN) for model reduction problems. The new approach is based on the assumption that the unobserved training data y at input x, lies within a set F x y f x y f x ( ) : ( ) ( ) = ! ! " # where F(x) is known from experience or past simulations. The new approach finds the location of the new sample such that the worst case error between the output of the resulting RBF ANN and the bounds of the unknown data as specified by F(x) is minimized. This paper illustrates the new approach for the case when x " R1. It was found that it is possible to find a good location for the new data sample by using the suggested approach in certain cases. A comparative study was also done indicating that the new experiment design approach is a good complement to the existing ones such as cross validation design and maximum minimum design.
format Article
author Abdullah, S. S.
Allwright, J. C.
author_facet Abdullah, S. S.
Allwright, J. C.
author_sort Abdullah, S. S.
title An active learning approach for radial basis function neural networks
title_short An active learning approach for radial basis function neural networks
title_full An active learning approach for radial basis function neural networks
title_fullStr An active learning approach for radial basis function neural networks
title_full_unstemmed An active learning approach for radial basis function neural networks
title_sort active learning approach for radial basis function neural networks
publisher Penerbit UTM Press
publishDate 2006
url http://eprints.utm.my/id/eprint/4112/1/JTD_2005_29.pdf
http://eprints.utm.my/id/eprint/4112/
http://www.penerbit.utm.my/onlinejournal/45/D/JTDis45D05.pdf
_version_ 1643643971072688128
score 13.244404

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