Identification of Wiener model using genetic algorithms

This paper investigates the use of genetic algorithms in the identification of Wiener model. The parameters describing the linear system and the static nonlinearity are estimated from input-output measurements by minimizing the error between the actual and identified systems. Using genetic algorithms, systems with non-minimum phase characteristics can be identified. Simulation results reveal the effectiveness and robustness of the proposed identification algorithm.