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

Khizer Mehmood, Naveed Ishtiaq Chaudhary (), Khalid Mehmood Cheema, Zeshan Aslam Khan, Muhammad Asif Zahoor Raja, Ahmad H. Milyani and Abdulellah Alsulami
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Khizer Mehmood: Department of Electrical & Computer Engineering, International Islamic University, Islamabad 44000, Pakistan
Naveed Ishtiaq Chaudhary: Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Yunlin, Douliou 64002, Taiwan
Khalid Mehmood Cheema: Department of Electronic Engineering, Fatima Jinnah Women University, Rawalpindi 46000, Pakistan
Zeshan Aslam Khan: Department of Electrical & Computer Engineering, International Islamic University, Islamabad 44000, Pakistan
Muhammad Asif Zahoor Raja: Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Yunlin, Douliou 64002, Taiwan
Ahmad H. Milyani: Department of Electrical & Computer Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Abdulellah Alsulami: Department of Electrical & Computer Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia

Mathematics, 2023, vol. 11, issue 11, 1-20

Abstract: 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.

Keywords: Hammerstein nonlinear; parameter identification; swarm intelligence; nonlinear heuristics (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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