Artificial gorilla troops optimization algorithm for key term separated input nonlinear output error system identification

Artificial gorilla troops optimization algorithm for key term separated input nonlinear output error system identification

This research study gives an overview of the identification of an input non-linear output error system (IN-OES) leveraging an artificial gorilla troops optimization algorithm (AGTOA). The key term, separated identification auxiliary-based model IN-OES, with the heuristic optimization algorithm AGTOA, is implemented to predict the parameters of the system. The efficiency of the swarm-inspired algorithm AGTOA is exploited for the parameter prediction of the IN-OES system, and promising outcomes were observed through the evaluation of fast convergence, prediction of real parameters of the system, and high fitness values, such as best fitness values, average fitness values, worst fitness values, and standard deviation values in no-noise and multiple-noise sce-narios, i.e., .0, 0.00021, 0.0022, 0.023.