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 nonlinearityis implemented through two static nonlinear blocks where a linear dynamic block is surrounded by two nonlinear staticsystems. Algorithms such as genetic algorithm can find unknown parameters, but the complexity of the calculations is their weakness. Hence, a class of computational methods namedParticle Swarm Optimization (PSO) is used. To avoid trapping in local optimum and improve performance; Adaptive WeightedParticle Swarm Optimization (AWPSO) method is used. The training method is responsible for finding the optimal values ofthe parameters of the transfer function from the linear dynamic part as well as the coefficients of the nonlinear static functions

کلیدواژه ها:

System identification ، Nonlinear system ، Hammerstein-Wiener model ، AWPSO