Study on identification algorithm of a class of nonlinear model

A parameter identification method of Hammerstein model with two-segment piecewise nonlinearities is studied in this paper. Firstly, expressing output of the nonlinear Hammerstein model as a regressive equation in all parameters via the key term separation principle and separating key term from linear block and nonlinear block. Secondly, the unknown true outputs in the information vector are replaced with the outputs of an auxiliary model, the unknown internal variables and the unmeasured noise terms are replaced with the estimated internal variables and the estimated residuals, respectively. Accordingly, the problem of the nonlinear system identification is cast as a function optimization over parameter space, and then an improved particle swarm optimization (IPSO) algorithm is adopted to solve the optimization problem. Finally, simulation results show the effectiveness of the presented identification algorithm.