An identification approach of Hammerstein model

Abstract

An identification method of Hammerstein model is investigated in this paper. First of all, the key term separation technique is introduced. Next, an auxiliary model is established. Accordingly, the identification problem of the Hammerstein model is cast as nonlinear function optimization problem over parameter space. Then, the estimation values of the parameters of the model are obtained based on particle swarm optimization (PSO) algorithm. In order to further enhance the precision and stability of the identification algorithm, a modified particle swarm optimization (MPSO) algorithm is applied to search the parameter space to find the optimal parametric estimation values of the model. Finally, simulation experiments show that the proposed algorithm is effective and reasonable.