Design of Nonlinear Marine Predator Heuristics for Hammerstein Autoregressive Exogenous System Identification with Key-Term Separation

Swarm-based metaheuristics have shown significant progress in solving different complex optimization problems, including the parameter identification of linear, as well nonlinear, systems. Nonlinear systems are inherently stiff and difficult to optimize and, thus, require special attention effectively estimate their parameters. This study investigates an input nonlinear autoregressive exogenous (IN-ARX) model through swarm intelligence knacks marine predators’ algorithm (NMPA). A detailed comparative analysis NMPA with other recently introduced metaheuristics, such Aquila optimizer, prairie dog optimization, reptile search algorithm, sine cosine whale established superiority proposed scheme terms accurate, robust, convergent performances for noise generation variations. The statistics generated multiple autonomous executions represent box whisker plots, along Wilcoxon rank-sum test, further confirming reliability stability estimation IN-ARX