Investigation of Adjustable Radial Basis Function estimations for Non-Linear System

This paper gives idea about design method for adaptive neural controller is proposed and it applied to the non-linear system Continuous stirred tank reactor CSTR. The investigating used in this designed tuned with process. To analyze performance of effect foot print uncertainty on controllers’ two various types algorithms namely state feedback control observer based are Radial basis function Neural network utilized approximation nonlinear . Software validation result suggested discussed below.