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

Automated summary

This study investigates the parameter identification of an input nonlinear autoregressive exogenous (IN-ARX) model using swarm intelligence knacks of the nonlinear marine predators' algorithm (NMPA). A comparative analysis of the NMPA with other recent metaheuristics establishes its superiority in terms of accurate, robust, and convergent performances for different noise and generation variations. The statistics generated through multiple autonomous executions further confirm the reliability and stability of the NMPA for parameter estimation of IN-ARX systems.