DocumentCode :
1123006
Title :
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
Link To Document :
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