Two-stage Recursive Least Squares Parameter Estimation Algorithm for Multivariate Output-error Autoregressive Moving Average Systems

This paper focuses on the parameter estimation problem of multivariate output-error autoregressive moving average (M-OEARMA) systems. By applying the auxiliary model identification idea and the decomposition technique, we derive a two-stage recursive least squares algorithm for estimating the M-OEAR...

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Veröffentlicht in:International journal of control, automation, and systems Jg. 17; H. 6; S. 1547 - 1557
Hauptverfasser: Guo, Yunze, Wan, Lijuan, Xu, Ling, Ding, Feng, Alsaedi, Ahmed, Hayat, Tasawar
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Bucheon / Seoul Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers 01.06.2019
Springer Nature B.V
제어·로봇·시스템학회
Schlagworte:
Algorithms
Autoregressive moving average
Computer simulation
Control
Economic models
Engineering
Estimating techniques
Least squares
Mathematical models
Mechatronics
Parameter estimation
Regular Papers
Robotics
제어계측공학
decomposition technique
parameter estimation
recursive identification
Auxiliary model
least squares
ISSN:1598-6446, 2005-4092
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:This paper focuses on the parameter estimation problem of multivariate output-error autoregressive moving average (M-OEARMA) systems. By applying the auxiliary model identification idea and the decomposition technique, we derive a two-stage recursive least squares algorithm for estimating the M-OEARMA system. Compared with the auxiliary model based recursive least squares algorithm, the proposed algorithm possesses higher identification accuracy. The simulation results confirm the effectiveness of the proposed algorithm.
Bibliographie:ObjectType-Article-1
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http://link.springer.com/article/10.1007/s12555-018-0512-0
ISSN:1598-6446
2005-4092
DOI:10.1007/s12555-018-0512-0