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A parameter optimization method for radial basis function type models

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4 Author(s)
Hui Peng ; Coll. of Inf. Sci. & Eng., Central South Univ., China ; T. Ozaki ; V. Haggan-Ozaki ; Y. Toyoda

This paper considers the nonlinear systems modeling problem for control. A structured nonlinear parameter optimization method (SNPOM) adapted to radial basis function (RBF) networks and an RBF network-style coefficients autoregressive model with exogenous variable model parameter estimation is presented. This is an off-line nonlinear model parameter optimization method, depending partly on the Levenberg-Marquardt method for nonlinear parameter optimization and partly on the least-squares method using singular value decomposition for linear parameter estimation. When compared with some other algorithms, the SNPOM accelerates the computational convergence of the parameter optimization search process of RBF-type models. The usefulness of this approach is illustrated by means of several examples.

Published in:

IEEE Transactions on Neural Networks  (Volume:14 ,  Issue: 2 )