Wiener Model Identification Based on Ade Algorithm

DE algorithm is a population-based heuristic global search technology. The algorithm has simple principle, fewer control parameters, but has strong robustness, and good optimization performance. This paper uses differential evolution algorithm for parameters identification of Wiener model. Firstly, we analyze the influence of mutation rate F on global parallel search ability and convergence in the process of identification. Secondly, an adaptive mutated differential evolution algorithm (ADE) is proposed. The algorithm keeps individual diversity to avoid premature convergence during the early stage and reduces the mutation rate gradually so as not to damage the optimal solution obtained during the later stage of the search process. Finally numerical simulation is performed on Wiener model. The results show that ADE algorithm has more effectiveness in parameter identification problem than PSO. On the other hand, compared with the general DE algorithm, ADE algorithm identifies the parameters of Wiener model with higher precision as well as shows lower sensitivity to the algorithmic parameters.