Wiener model identification by evolutionary computation approach with piecewise linearization

We address a novel approach to identify a nonlinear dynamic system for Wiener models, which are composed of a linear dynamic system part followed by a nonlinear static part. The aim of the system identification is to provide an optimal mathematical model for both the linear dynamic and nonlinear static parts in an appropriate sense. Assuming the nonlinear static part is invertible, we approximate the inverse function by a piecewise linear function. We estimate the piecewise linear inverse function by using the evolutionary computation approach such as the genetic algorithm and evolution strategies, and estimate the linear dynamic system part by the least squares method. The results of numerical simulation studies indicate the usefulness of proposed approach to the Wiener model identification.