Identification of a Hammerstein Model Equipped with a Static Nonlinear Odd Function

Abstract

This paper studies a procedure for identifying a Hammerstein model equipped with a static nonlinear odd function, where the only output is measured and the input is white Gaussian. A statistical property of the process generated by the static nonlinear odd function with a white Gaussian input is analyzed by means of a moment generating function. A proposed procedure for identifying the Hammerstein model consists of two steps : The first step is to identify the linear dynamical part via a stochastic subspace identification method based on a block LQ-decomposition; the second step is to estimate the static nonlinear odd function by using distribution functions. Numerical simulation results are also included.