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

Authors :
Khizer Mehmood
Naveed Ishtiaq Chaudhary
Khalid Mehmood Cheema
Zeshan Aslam Khan
Muhammad Asif Zahoor Raja
Ahmad H. Milyani
Abdulellah Alsulami
Source :
Mathematics, Vol 11, Iss 11, p 2512 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

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.

Details

Language :
English
ISSN :
22277390
Volume :
11
Issue :
11
Database :
Directory of Open Access Journals
Journal :
Mathematics
Publication Type :
Academic Journal
Accession number :
edsdoj.b0893c38619b4f00a64c12c713122ad1
Document Type :
article
Full Text :
https://doi.org/10.3390/math11112512