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Construction of tunable radial basis function networks using orthogonal forward selection

Chen, S., Hong, X., Luk, B. L. and Harris, C. 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. ISSN 1083-4419

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To link to this item DOI: 10.1109/tsmcb.2008.2006688

Abstract/Summary

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 an RBF node, namely, its center vector and diagonal covariance matrix, by minimizing 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 generalize 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.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:15173
Uncontrolled Keywords:Classification, leave-one-out (LOO) statistics, orthogonal forward, selection (OFS), radial basis function (RBF) network, regression, tunable node, SIGNAL-PROCESSING APPLICATIONS, NEURAL-NETWORKS, LEAST-SQUARES, SYSTEM-IDENTIFICATION, RBF NETWORKS, ALGORITHM, OPTIMIZATION, PARAMETERS, MANIPULATORS, EQUALIZATION

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