Evolutionary computation approach to block oriented nonlinear model identification

Wiener model and Hammerstein model are block oriented nonlinear models with the cascade connection of static non-linear part and linear dynamic part. Though they have very simple model structure, they can represent and approximate many real processes in electrical, chemical and biological engineering. In this paper, a novel approach for nonlinear system identification is addressed for the parameterized block oriented models such as Hammerstein and Wiener models. Approximating the nonlinear static part or its inverse by a piecewise linear function, its parameters are estimated by using the evolutionary computation approach such as genetic algorithm (GA) and evolution strategies (ES), while the linear dynamic system part is estimated by the least squares method. Numerical simulation studies illustrate the applicability of the proposed approach.