Parallel Wiener identification starting from linearized models

This paper proposes a parametric identification method for parallel Wiener systems, starting from linearized models. Nonlinear models are often hard to obtain, while nonlinear measurements benches are hard to design. The method proposed in this paper starts from linear measurements and ends with obtaining a good nonlinear model. First, a two-dimensional linear model depending on the input frequency and input signal power is measured. Next, the initial estimates of the linear time invariant blocks of the nonlinear model are obtained by extracting information of the dependency about the frequency dynamics on the input power. The static nonlinearities of the model are estimated using a linear least squares approach. Finally, these initial estimates are further refined using nonlinear optimization. The method is illustrated on a simulation and a validation measurement example to illustrate the theoretical efficiency and the practical usefulness of the estimator.