A Robust Hammerstein-Wiener Model Identification Method for Highly Nonlinear Systems

The existing results show the applicability of Over-Parameterized Model based Hammerstein-Wiener model identification methods. However, it requires to estimate extra parameters and performer a low rank approximation step. Therefore, may give rise unnecessarily high variance in parameter estimates for highly nonlinear systems, especially using small noisy data set. To overcome this corruptive phenomenon. phenomenon, paper, robust method is developed systems when set, where two parsimonious parametrization models with fewer are used, an iteration then used retrieve true system from models. Such modification can improve estimation performance terms accuracy compared over-parametrization All above-mentioned developments analyzed analysis, along simulation example confirm effectiveness.