Nonlinear system identification of Hammerstein-Wiener model using AWPSO

This paper presents the problem of constructing an appropriate model with Hammerstein-Wiener structure for nonlinear system identification. In this structure, the nonlinearity is implemented through two static nonlinear blocks where a linear dynamic block is surrounded by two nonlinear static systems. Algorithms such as genetic algorithm can find unknown parameters, but the complexity of the calculations is their weakness. Hence, a class of computational methods named Particle Swarm Optimization (PSO) is used. To avoid trapping in local optimum and improve performance; Adaptive Weighted Particle Swarm Optimization (AWPSO) method is used. The training method is responsible for finding the optimal values of the parameters of the transfer function from the linear dynamic part as well as the coefficients of the nonlinear static functions.