A gazelle optimization expedition for key term separated fractional nonlinear systems with application to electrically stimulated muscle modeling

Various real-time processes are effectively represented with a fractional nonlinear autoregressive exogenous (F-NARX) model where estimation of model parameters is considered as an essential task. In this study, a population-based gazelle optimization algorithm (GOA) inspired by the evolutionary cha...

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Veröffentlicht in:Chaos, solitons and fractals Jg. 185; S. 115111
Hauptverfasser: Khan, Taimoor Ali, Chaudhary, Naveed Ishtiaq, Hsu, Chung-Chian, Mehmood, Khizer, Khan, Zeshan Aslam, Raja, Muhammad Asif Zahoor, Shu, Chi-Min
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.08.2024
Schlagworte:
Fractional calculus
Gazelle optimization algorithm
Nonlinear systems
Swarm intelligence
Swarm intelligence
Gazelle optimization algorithm
Nonlinear systems
Fractional calculus
ISSN:0960-0779
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Zusammenfassung:Various real-time processes are effectively represented with a fractional nonlinear autoregressive exogenous (F-NARX) model where estimation of model parameters is considered as an essential task. In this study, a population-based gazelle optimization algorithm (GOA) inspired by the evolutionary characteristics of gazelle's is exploited for the parameter estimation of the key term-separated F-NARX system. The Grünwald-Letnikov derivative, a fractional order calculus operator is integrated to develop the F-NARX system from a conventional non-linear autoregressive system. The mean square error-based merit function is developed and the effectiveness of the GOA for the F-NARX system is analyzed in terms of speedy convergence, estimation accuracy, complexity and robustness for different noise scenarios. The extendibility of the GOA is assessed through the estimation of stiff parameters of the electrically stimulated muscle model (ESMM) required for rehabilitation of paralyzed muscles. The efficacy of the GOA is endorsed through Wilcoxon signed rank statistical test in comparison with recent counterparts of Runge Kutta optimization algorithm, Whale optimization algorithm, and Harris Hawks optimization algorithm. •Gazelle optimization algorithm, GOA, is presented for parameter estimation of the fractional nonlinear, F-NARX systems.•The mean square error-based merit function is developed to evaluate the effectiveness of the GOA.•The robustness, convergence, and accuracy of the GOA are endorsed for electrically stimulated muscle model identification.•The efficacy of the GOA is established by statistical testing in comparison with the state-of-the-art counterparts.
ISSN:0960-0779
DOI:10.1016/j.chaos.2024.115111