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Journal of Zhejiang University SCIENCE C 1998 Vol.-1 No.-1 P.

http://doi.org/10.1631/FITEE.2400730


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 KHAN4, 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



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 2nd and 3rd -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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