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Construction of tunable radial basis function networks using orthogonal forward selection

Construction of tunable radial basis function networks using orthogonal forward selection
Construction of tunable radial basis function networks using orthogonal forward selection
An orthogonal forward selection (OFS) algorithm based on leave-one-out (LOO) criteria is proposed for the construction of radial basis function (RBF) networks with tunable nodes. Each stage of the construction process determines a RBF node, namely its centre vector and diagonal covariance matrix, by minimising the LOO statistics. For regression application, the LOO criterion is chosen to be the LOO mean square error, while the LOO misclassification rate is adopted in two-class classification application. This OFS-LOO algorithm is computationally efficient and it is capable of constructing parsimonious RBF networks that generalise well. Moreover, the proposed algorithm is fully automatic and the user does not need to specify a termination criterion for the construction process. The effectiveness of the proposed RBF network construction procedure is demonstrated using examples taken from both regression and classification applications.
1083-4419
457-466
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Hong, Xia
e6551bb3-fbc0-4990-935e-43b706d8c679
Luk, Bing L.
7f992721-74f4-4a2d-b990-afcece627189
Harris, Chris J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Hong, Xia
e6551bb3-fbc0-4990-935e-43b706d8c679
Luk, Bing L.
7f992721-74f4-4a2d-b990-afcece627189
Harris, Chris J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a

Chen, Sheng, Hong, Xia, Luk, Bing L. and Harris, Chris J. (2009) Construction of tunable radial basis function networks using orthogonal forward selection IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 39, (2), pp. 457-466.

Record type: Article

Abstract

An orthogonal forward selection (OFS) algorithm based on leave-one-out (LOO) criteria is proposed for the construction of radial basis function (RBF) networks with tunable nodes. Each stage of the construction process determines a RBF node, namely its centre vector and diagonal covariance matrix, by minimising the LOO statistics. For regression application, the LOO criterion is chosen to be the LOO mean square error, while the LOO misclassification rate is adopted in two-class classification application. This OFS-LOO algorithm is computationally efficient and it is capable of constructing parsimonious RBF networks that generalise well. Moreover, the proposed algorithm is fully automatic and the user does not need to specify a termination criterion for the construction process. The effectiveness of the proposed RBF network construction procedure is demonstrated using examples taken from both regression and classification applications.

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Published date: April 2009
Organisations: Southampton Wireless Group

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Local EPrints ID: 267200
URI: https://eprints.soton.ac.uk/id/eprint/267200
ISSN: 1083-4419
PURE UUID: 8279b492-233b-4d4a-b4f6-90df4499879d

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Date deposited: 20 Mar 2009 16:14
Last modified: 12 Sep 2017 16:33

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Contributors

Author: Sheng Chen
Author: Xia Hong
Author: Bing L. Luk
Author: Chris J. Harris

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