Modelling of Wiener-Hammerstein Systems via the Best Linear Approximation

In this paper, we focus on the nonlinear modelling of Wiener-Hammerstein structures. The most difficult step in the identification process of such block-oriented models is to generate good initial values for the linear dynamic blocks. Here, good initial estimates are obtained by using the Best Linear Approximation of the system under test, which can easily be extracted from the data. The idea is to write the linear dynamic blocks as a linear combination of basis functions containing the poles and the zeros of the Best Linear Approximation. Next, these initial values are further tuned via a nonlinear optimization procedure. The proposed identification method is applied to real measurement data.