A hybrid backtracking search algorithm with wavelet mutation-based nonlinear system identification of Hammerstein models

Abstract

In this paper a novel and accurate approach is presented to identify varieties of nonlinear Hammerstein models (closed loop and open loop) with the help of an optimization algorithm that combines a recently proposed backtracking search algorithm with wavelet theory-based mutation scheme (BSA-WM). The optimum output MSE associated with each plant along with its statistical information justifies the better precision and accuracy of BSA-WM-based identification approach as compared to the other methods reported in earlier literature.