Gaussian information matrix for Wiener model identification

Abstract

We present a closed form expression for the information matrix associated with the Wiener model identification problem under the assumption that the input signal is a stationary Gaussian process. This expression holds under quite generic assumptions. We allow the linear sub-system to have a rational transfer function of arbitrary order, and the static nonlinearity to be a polynomial of arbitrary degree. We also present a simple expression for the determinant of the information matrix. The expressions presented herein has been used for optimal experiment design for Wiener model identification.