Identification of Continuous-time Hammerstein Systems Using Automatic Choosing Function Model and PSO

In this paper an identification method of continuous-time Hammerstein systems is proposed by using automatic choosing function (ACF) model and particle swarm optimization (PSO). An unknown nonlinear static part to be estimated is approximately represented by the ACF model. The weighting parameters of the ACF and the system parameters of the linear dynamic part are estimated by the least-squares method, while the adjusting parameters of the ACF model structure are determined by PSO. Simulation results are shown to illustrate the proposed method