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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| Format: | Article |
| Language: | en |
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Penerbit UTM Press
2006
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| Online Access: | http://eprints.utm.my/4112/1/JTD_2005_29.pdf http://eprints.utm.my/4112/ http://www.penerbit.utm.my/onlinejournal/45/D/JTDis45D05.pdf |
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| _version_ | 1845471042869395456 |
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| author | Abdullah, S. S. Allwright, J. C. |
| author_facet | Abdullah, S. S. Allwright, J. C. |
| author_sort | Abdullah, S. S. |
| building | UTM Library |
| collection | Institutional Repository |
| content_provider | Universiti Teknologi Malaysia |
| content_source | UTM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| 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.
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| format | Article |
| id | my.utm.eprints-4112 |
| institution | Universiti Teknologi Malaysia |
| language | en |
| publishDate | 2006 |
| publisher | Penerbit UTM Press |
| record_format | eprints |
| spelling | my.utm.eprints-41122017-11-01T04:17:28Z http://eprints.utm.my/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/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 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Abdullah, S. S. Allwright, J. C. An active learning approach for radial basis function neural networks |
| title | 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_short | An active learning approach for radial basis function neural networks |
| title_sort | active learning approach for radial basis function neural networks |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://eprints.utm.my/4112/1/JTD_2005_29.pdf http://eprints.utm.my/4112/ http://www.penerbit.utm.my/onlinejournal/45/D/JTDis45D05.pdf |
| url_provider | http://eprints.utm.my/ |