Construction of tunable radial basis function networks using orthogonal forward selection

Authors:
Sheng Chen;Xia Hong;Bing L. Luk;Chris J. Harris
Affiliations:
School of Electronics and Computer Science, University of Southampton, Southampton, UK;School of Systems Engineering, University of Reading, Reading, UK;Department of Manufacturing Engineering and Engineering Management, City University of Hong Kong, Kowloon, Hong Kong, China;School of Electronics and Computer Science, University of Southampton, Southampton, UK
Venue:
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Year:
2009

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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 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.