RADIAL BASIS FUNCTION NEURAL-NETWORK FOR APPROXIMATION AND ESTIMATIONOF NONLINEAR STOCHASTIC DYNAMIC-SYSTEMS

This paper presents a means to approximate the dynamic and static equa tions of stochastic nonlinear systems and to estimate state variables based on Radial Basis Function Neural Network (RBFNN). After a nonpara metric approximate model of the system is constructed from a priori ex periments or simulations, a suboptimal filter is designed based on the upper bound error in approximating the original unknown plant with no nlinear state and output equations. The procedures for both training a nd state estimation are described along with discussions on approximat ion error. Nonlinear systems with linear output equations are consider ed as a special case of the general formulation. Finally, applications of the proposed RBFNN to the state estimation of highly nonlinear sys tems are presented to demonstrate the performance and effectiveness of the method.