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Swarm intelligent computing of electric eel foraging heuristics for fractional Hammerstein autoregressive exogenous noise model identification
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Faisal ALTAF1, Ching-Lung CHANG2, Naveed Ishtiaq CHAUDHARY3, Taimoor Ali KHAN4, Zeshan Aslam KHAN4,5, 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, 1998, -1(-1): .
@article{title="Swarm intelligent computing of electric eel foraging heuristics for fractional Hammerstein autoregressive exogenous noise model identification",
author="Faisal ALTAF1, Ching-Lung CHANG2, Naveed Ishtiaq CHAUDHARY3, Taimoor Ali KHAN4, Zeshan Aslam KHAN4,5, Chi-Min SHU6, Muhammad Asif Zahoor RAJA3",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="-1",
number="-1",
pages="",
year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2400730"
}
%0 Journal Article
%T Swarm intelligent computing of electric eel foraging heuristics for fractional Hammerstein autoregressive exogenous noise model identification
%A Faisal ALTAF1
%A Ching-Lung CHANG2
%A Naveed Ishtiaq CHAUDHARY3
%A Taimoor Ali KHAN4
%A Zeshan Aslam KHAN4
%A 5
%A Chi-Min SHU6
%A Muhammad Asif Zahoor RAJA3
%J Journal of Zhejiang University SCIENCE C
%V -1
%N -1
%P
%@ 2095-9184
%D 1998
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2400730
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T1 - Swarm intelligent computing of electric eel foraging heuristics for fractional Hammerstein autoregressive exogenous noise model identification
A1 - Faisal ALTAF1
A1 - Ching-Lung CHANG2
A1 - Naveed Ishtiaq CHAUDHARY3
A1 - Taimoor Ali KHAN4
A1 - Zeshan Aslam KHAN4
A1 - 5
A1 - Chi-Min SHU6
A1 - Muhammad Asif Zahoor RAJA3
J0 - Journal of Zhejiang University Science C
VL - -1
IS - -1
SP -
EP -
%@ 2095-9184
Y1 - 1998
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2400730
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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