EM algorithm-based identification of a class of nonlinear Wiener systems with missing output data

Abstract

This paper is concerned with the problem of parameter estimation for nonlinear Wiener systems in the stochastic framework. Based on the expectation–maximization (EM) algorithm in dealing with the incomplete data, it is applied to estimate the parameters of nonlinear Wiener models considering the randomly missing outputs. By means of the EM approach, the parameters and the missing outputs can be estimated simultaneously. To obtain the noise-free output in the linear subsystem of the Wiener model, the auxiliary model identification idea is adopted here. The simulation results indicate the effectiveness of the proposed approach for identification of a class of nonlinear Wiener models.