Continuous-Time Identification of Nonlinear Systems Using Radial Basis Function Network Model and Genetic Algorithm

Abstract

This paper deals with an identification method of continuous-time nonlinear systems based on a continuous-time radial basis function (RBF) network model. The higher order derivatives of input and output data are estimated by a delayed state variable filter, or the Butterworth filter. An unknown function of nonlinear term of the objective system is assumed to be approximately represented by an RBF network. The structure of the RBF network model is properly determined by using a genetic algorithm (GA). Moreover the cutoff frequency of the state variable filter are also designed by GA. The unknown weighting parameters of the RBF network and system parameters in the linear terms are estimated by the least-squares method. Simulation results are shown to demonstrate the effectiveness of the proposed method.