Radial basis function neural network for approximation and estimation of nonlinear stochastic dynamic systems

Author

Elanayar V.T., S. ; Shin, Yung C.

Author_Institution

Sch. of Mech. Eng., Purdue Univ., West Lafayette, IN, USA

Volume

5

Issue

4

fYear

1994

fDate

7/1/1994 12:00:00 AM

Firstpage

594

Lastpage

603

Abstract

This paper presents a means to approximate the dynamic and static equations of stochastic nonlinear systems and to estimate state variables based on radial basis function neural network (RBFNN). After a nonparametric approximate model of the system is constructed from a priori experiments or simulations, a suboptimal filter is designed based on the upper bound error in approximating the original unknown plant with nonlinear state and output equations. The procedures for both training and state estimation are described along with discussions on approximation error. Nonlinear systems with linear output equations are considered as a special case of the general formulation. Finally, applications of the proposed RBFNN to the state estimation of highly nonlinear systems are presented to demonstrate the performance and effectiveness of the method

Keywords

feedforward neural nets; filtering and prediction theory; nonlinear systems; optimisation; state estimation; stochastic systems; approximation error; dynamic equations; linear output equations; nonlinear stochastic dynamic systems; nonparametric approximate model; radial basis function neural network; state estimation; static equations; suboptimal filter; upper bound error; Filters; Least squares approximation; Neural networks; Nonlinear dynamical systems; Nonlinear equations; Nonlinear systems; Radial basis function networks; State estimation; Stochastic processes; Stochastic systems;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/72.298229

Filename

298229