Variants of Chaotic Grey Wolf Heuristic for Robust Identification of Control Autoregressive Model

Khizer Mehmood; Naveed Ishtiaq Chaudhary; Zeshan Aslam Khan; Khalid Mehmood Cheema; Muhammad Asif Zahoor Raja

doi:10.3390/biomimetics8020141

Variants of Chaotic Grey Wolf Heuristic for Robust Identification of Control Autoregressive Model

Biomimetics (Basel). 2023 Mar 30;8(2):141. doi: 10.3390/biomimetics8020141.

Authors

Khizer Mehmood¹, Naveed Ishtiaq Chaudhary², Zeshan Aslam Khan¹, Khalid Mehmood Cheema³, Muhammad Asif Zahoor Raja²

Affiliations

¹ Department of Electrical and Computer Engineering, International Islamic University, Islamabad 44000, Pakistan.
² Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou 64002, Taiwan.
³ Department of Electronic Engineering, Fatima Jinnah Women University, Rawalpindi 46000, Pakistan.

Abstract

In this article, a chaotic computing paradigm is investigated for the parameter estimation of the autoregressive exogenous (ARX) model by exploiting the optimization knacks of an improved chaotic grey wolf optimizer (ICGWO). The identification problem is formulated by defining a mean square error-based fitness function between true and estimated responses of the ARX system. The decision parameters of the ARX model are calculated by ICGWO for various populations, generations, and noise levels. The comparative performance analyses with standard counterparts indicate the worth of the ICGWO for ARX model identification, while the statistical analyses endorse the efficacy of the proposed chaotic scheme in terms of accuracy, robustness, and reliability.

Keywords: ARX; chaos; grey wolf optimizer; parameter estimation.

Grants and funding

This research received no external funding.