Mountain Gazelle optimization algorithm for identification of nonlinear Hammerstein output error systems

Mountain Gazelle optimization algorithm for identification of nonlinear Hammerstein output error systems

This study provides an inclusive review of the vigorous identification of the Hammerstein-output error system (HOES). The mean-square-error-based fitness function is used to explore the efficacy of the mountain gazelle optimization algorithm (MGO). The auxiliary-model with the key-term separation principle is combined to approximate the parameters for accurately identifying complex parameters of the system. The efficacy of the nature-inspired heuristic optimization algorithm i.e., the mountain gazelle op¬timization algorithm is exploited for the Hammerstein-output error system (HOES) and is evaluated through accuracy, convergence speed, and estimation of actual parameters as compared with three states of the art algorithms that are whale-optimization algorithm (WOA), grey-wolf optimization algorithm (GWO), and arithmetic-optimization algorithm (AO).