A parameter optimization method for radial basis function type models

Author

Peng, Hui ; Ozaki, Tohru ; Haggan-Ozaki, Valerie ; Toyoda, Yukihiro

Author_Institution

Coll. of Inf. Sci. & Eng., Central South Univ., China

Volume

14

Issue

2

fYear

2003

fDate

3/1/2003 12:00:00 AM

Firstpage

432

Lastpage

438

Abstract

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.

Keywords

autoregressive processes; convergence; nonlinear systems; optimisation; parameter estimation; radial basis function networks; singular value decomposition; Levenberg-Marquardt method; RBF neural network; autoregressive model; exogenous variable; identification; nonlinear systems; optimization; parameter estimation; radial basis function network; singular value decomposition; state-dependent model; Function approximation; Nonlinear control systems; Nonlinear systems; Optimization methods; Parameter estimation; Power system dynamics; Power system modeling; Power system reliability; Radial basis function networks; Thermal variables control;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/TNN.2003.809395

Filename

1189640