Identification of wiener systems using reduced complexity volterra models

Abstract

In this paper we study the identification problem of single-input, single-output Wiener systems with polynomial nonlinearities. Wiener systems can be represented by a cascade of a dynamic linear subsystem followed by a static nonlinearity. Our approach is to use a reduced complexity Volterra model structure called fixed pole expansion technique (FPET) to estimate the products of the coefficients of the nonlinearity and the linear subsystems coefficients. We then present a method using the singular value decomposition to extract the coefficients of the non linearity and of the dynamic linear subsystem.