Convergence of the auxiliary model-based multi-innovation generalized extended stochastic gradient algorithm for Box–Jenkins systems

This paper focuses on the parameter estimation problem of Box–Jenkins systems. Using the multi-innovation identification theory, an auxiliary model-based multi-innovation generalized extended stochastic gradient algorithm is derived. The convergence of the proposed algorithm is analyzed based on the...

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Veröffentlicht in:Nonlinear dynamics Jg. 82; H. 1-2; S. 269 - 280
Hauptverfasser: Wang, Xuehai, Ding, Feng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Dordrecht Springer Netherlands 01.10.2015
Springer Nature B.V
Schlagworte:
Algorithms
Automotive Engineering
Classical Mechanics
Computer simulation
Control
Convergence
Dynamical Systems
Economic models
Engineering
Estimating techniques
Innovations
Martingales
Mathematical models
Mechanical Engineering
Original Paper
Parameter estimation
Parameter identification
Vibration
Box–Jenkins system
Recursive identification
Gradient search
Parameter estimation
Performance analysis
ISSN:0924-090X, 1573-269X
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Zusammenfassung:This paper focuses on the parameter estimation problem of Box–Jenkins systems. Using the multi-innovation identification theory, an auxiliary model-based multi-innovation generalized extended stochastic gradient algorithm is derived. The convergence of the proposed algorithm is analyzed based on the stochastic martingale theory. It is proved that the parameter estimation errors converge to zero under persistent excitation conditions. Two simulation examples are provided to confirm the convergence results.
Bibliographie:ObjectType-Article-1
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content type line 14
ISSN:0924-090X
1573-269X
DOI:10.1007/s11071-015-2155-5