JZUS - Journal of Zhejiang University SCIENCE

Frontiers of Information Technology & Electronic Engineering

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Swarm intelligent computing of electric eel foraging heuristics for fractional Hammerstein autoregressive exogenous noise model identification

Author(s): Faisal ALTAF¹, Ching-Lung CHANG², Naveed Ishtiaq CHAUDHARY³, Taimoor Ali KHAN⁴, Zeshan Aslam KHAN^4, ⁵, Chi-Min SHU⁶, Muhammad Asif Zahoor RAJA³
Affiliation(s): ¹Editorial Graduate School of Engineering Science and Technology, National Yunlin University of Science and Technology, Yunlin 64002, Taiwan, China; more
Corresponding email(s): chaudni@yuntech.edu.tw
Key Words: Fractional calculus; Nonlinear systems; Electric eel foraging; Intelligent computing

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Faisal ALTAF¹, Ching-Lung CHANG², Naveed Ishtiaq CHAUDHARY³, Taimoor Ali KHAN⁴, Zeshan Aslam KHAN^4,⁵, Chi-Min SHU⁶, Muhammad Asif Zahoor RAJA³. Swarm intelligent computing of electric eel foraging heuristics for fractional Hammerstein autoregressive exogenous noise model identification[J]. Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/FITEE.2400730

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author="Faisal ALTAF¹, Ching-Lung CHANG², Naveed Ishtiaq CHAUDHARY³, Taimoor Ali KHAN⁴, Zeshan Aslam KHAN^4,⁵, Chi-Min SHU⁶, Muhammad Asif Zahoor RAJA³",
journal="Frontiers of Information Technology & Electronic Engineering",
year="in press",
publisher="Zhejiang University Press & Springer",
doi="https://doi.org/10.1631/FITEE.2400730"
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%T Swarm intelligent computing of electric eel foraging heuristics for fractional Hammerstein autoregressive exogenous noise model identification
%A Faisal ALTAF¹
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%A Chi-Min SHU⁶
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Abstract
Chinese Summary
Academic Network
Reviewer Comment

Abstract: The knacks of fractional calculus are considered a useful tool to obtain a deeper insight into systems considering the memory effect or previous history. Fractional order modeling of nonlinear systems may increase the stiffness and complexity of the system but also provides better insights. This study introduces swarm intelligence-based parameter estimation of the fractional Hammerstein autoregressive exogenous noise (fractional-HARX) model. The Grunwald–Letnikov finite difference formula is used to develop the fractional-HARX model from the standard Hammerstein autoregressive exogenous noise model. This study presents the design of a swarm intelligence-based electric eel foraging optimization algorithm (EEFOA) for parameter estimation of the fractional-HARX model under multiple noise scenarios for 2^nd and 3^rd -order polynomial type nonlinearity. The key term separation principle is also incorporated in the system model to reduce the oc-currence of redundant parameters due to cross-product terms in the information vector. The designed methodology is ex-amined, and the superiority of EEFOA is endorsed in terms of convergence, robustness, stiff parameter estimation, and deviation from the mean point in comparison with state-of the-art optimization heuristics such as the whale optimization algorithm, the African vulture optimization algorithm, Harris hawk′s optimizer, and the reptile search algorithm. The statistical significance of the EEFOA for the estimation of fractional-HARX models is also established using statistical indices of best, mean, and worst fitness values along with standard deviation for multiple noise scenarios.

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