Parameter Identification of Wiener Model with Discontinuous Nonlinearities Using Hybrid Simplex Search and Particle Swarm Optimization

Yinggan Tang, Leijie Qiao, Xinping Guan Abstract This paper deals with the parameter identification of Wiener model with discontinuous nonlinear. The parameter identification problem is converted to an optimal problem with a suitable objective function. A hybrid optimal method, which integrates the Nelder-Mead simplex search and particle swarm optimization (NM-PSO), is used to optimize the objective function. The hybrid optimal method NM-PSO can get obtain the global optimal solution with fast convergent rate. Two illustrative examples are included to demonstrate the effectiveness and feasible of the proposed identification method.