Hammerstein system identification by non-parametric regression estimation

Abstract

A discrete-time, multiple-input non-linear Hammerstein system is identified. The dynamical subsystem is recovered using the standard correlation method. The main results concern estimation of the non-linear memoryless subsystem. No conditions concerning the functional form of the transform characteristic of the subsystem are made and an algorithm for estimation of the characteristic is given. The algorithm is simply a non-parametric kernel estimate of the regression function calculated from the dependent data. It is shown that the algorithm converges to the characteristic of the subsystem in the pointwise as well as the global sense. For sufficiently smooth characteristics, the rate of convergence is o(n^-1/(2+d in probability, where d is the dimension of the input variable.