Block oriented nonlinear model identification by evolutionary computation approach

Block oriented nonlinear models such as Hammerstein and Wiener models are composed by a cascade combination of a linear dynamic model and a static or memoryless nonlinear function. Though these have very simple model structures, they can represent and approximate many real processes especially in electrical, chemical and biological engineering. In this paper, a novel approach for nonlinear system identification is addressed for the block oriented models. After 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.