Hierarchical gradient parameter estimation algorithms for fractional order Wiener OEARMA system

In this paper, the identification of fractional order Wiener output error auto-regressive moving average (OEARMA) systems is discussed. The dynamic part of the Wiener OEARMA system is an OEARMA structure, and the static part is two-stage nonlinearity. The identification expression is obtained by key...

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Veröffentlicht in:Nonlinear dynamics Jg. 113; H. 15; S. 19579 - 19598
Hauptverfasser: Li, Junhong, Zhang, Hongrui, Xiao, Kang, Gu, Juping
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Dordrecht Springer Nature B.V 01.08.2025
Schlagworte:
Accuracy
Algorithms
Autoregressive moving average
Calculus
Engineering
Estimates
Parameter estimation
Parameter identification
ISSN:0924-090X, 1573-269X
Online-Zugang:Volltext
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Zusammenfassung:In this paper, the identification of fractional order Wiener output error auto-regressive moving average (OEARMA) systems is discussed. The dynamic part of the Wiener OEARMA system is an OEARMA structure, and the static part is two-stage nonlinearity. The identification expression is obtained by key term separation technique and definition of the Grünwald Letnikov factional differential. Then, the hierarchical extended stochastic gradient (H-ESG) and hierarchical multi-innovation extended stochastic gradient (H-MIESG) methods are proposed for identification of the unknown parameter in the system and the the convergence is verified. Through numerical simulations, the feasibility of the derived algorithms is studied. The identification accuracy of H-MIESG is satisfactory, which reflects its excellent identification efficiency.
Bibliographie:ObjectType-Article-1
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ISSN:0924-090X
1573-269X
DOI:10.1007/s11071-025-11187-z