Auxiliary Variable-Based Identification Algorithms for Uncertain-Input Models

This study presents two auxiliary variable-based identification algorithms for uncertain-input models. The auxiliary variable-based least squares algorithm can obtain unbiased parameter estimates by introducing suitable auxiliary variable vectors. Furthermore, an auxiliary variable-based recursive l...

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Veröffentlicht in:Circuits, systems, and signal processing Jg. 39; H. 7; S. 3389 - 3404
Hauptverfasser: Chen, Jing, Zhu, Quanmin, Chandra, Budi, Pu, Yan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.07.2020
Springer Nature B.V
Schlagworte:
Algorithms
Circuits and Systems
Computer simulation
Electrical Engineering
Electronics and Microelectronics
Engineering
Instrumentation
Least squares
Parameter estimation
Signal,Image and Speech Processing
Unbiased parameter estimate
Uncertain-input model
Computational effort
Auxiliary variable
Least squares algorithm
ISSN:0278-081X, 1531-5878
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Zusammenfassung:This study presents two auxiliary variable-based identification algorithms for uncertain-input models. The auxiliary variable-based least squares algorithm can obtain unbiased parameter estimates by introducing suitable auxiliary variable vectors. Furthermore, an auxiliary variable-based recursive least squares algorithm is proposed to reduce the computational efforts. To validate the framework and algorithms developed, it has conducted a series of bench tests with computational experiments. The simulated numerical results/plots are consistent with the analytically derived results in terms of the feasibility and effectiveness of the proposed procedure.
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ISSN:0278-081X
1531-5878
DOI:10.1007/s00034-019-01320-w