The automatic model selection and variable kernel width for RBF neural networks

doi:10.1016/j.neucom.2011.07.011

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The automatic model selection and variable kernel width for RBF neural networks

2011-10-01

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DOI：10.1016/j.neucom.2011.07.011

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Abstract

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The Orthogonal Least Squares (OLS) algorithm has been extensively used in basis selection for RBF networks, but it is unable to perform model selection automatically because the tolerance p must be specified manually. This introduces noise and it is difficult to implement in the parametric complexity of real-time system. Therefore, a generic criterion that detects the optimum number of its basis functions is proposed. In this paper, not only the Bayesian Information Criterion (BIC) method, used for fitness calculation, is incorporated into the basis function selection process of the OLS algorithm for assigning its appropriate number, but also a new method is developed to optimize the widths of the Gaussian functions in order to improve the generalization performance. The augmented algorithm is employed to the Radial Basis Function Neural Networks (RBFNN) for known and unknown noise nonlinear dynamic systems and its performance is compared with the standard OLS: experimental results show that both the efficacy of BIC for fitness calculation and the importance of proper choice of basis function widths are significant. (C) 2011 Elsevier B.V. All rights reserved.

Keywords:

Orthogonal least squares algorithm

Radial basis function network

Bayesian information criterion

Kernel width

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