Multi-innovation gradient estimation algorithms for multivariate equation-error autoregressive moving average systems based on the filtering technique

This study concentrates on the parameter estimation of multivariate pseudo-linear autoregressive moving average systems by means of the multi-innovation identification theory and data filtering technique. A multi-innovation stochastic gradient algorithm is derived by introducing the innovation lengt...

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Veröffentlicht in:IET control theory & applications Jg. 13; H. 13; S. 2086 - 2094
Hauptverfasser: Ma, Ping, Ding, Feng, Hayat, Tasawar
Format: Journal Article
Sprache:Englisch
Veröffentlicht: The Institution of Engineering and Technology 03.09.2019
Schlagworte:
autoregressive moving average processes
data filtering technique
filtering theory
gradient methods
multiinnovation gradient estimation algorithms
multiinnovation stochastic gradient algorithm
multivariate equation‐error autoregressive
multivariate pseudolinear autoregressive moving average systems
parameter estimation
Research Article
multiinnovation gradient estimation algorithms
autoregressive moving average processes
filtering theory
multivariate equation-error autoregressive
parameter estimation
data filtering technique
multivariate pseudolinear autoregressive moving average systems
gradient methods
multiinnovation stochastic gradient algorithm
ISSN:1751-8644, 1751-8652
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Beschreibung
Zusammenfassung:This study concentrates on the parameter estimation of multivariate pseudo-linear autoregressive moving average systems by means of the multi-innovation identification theory and data filtering technique. A multi-innovation stochastic gradient algorithm is derived by introducing the innovation length in the stochastic gradient algorithm. Then, the original system is transformed into two subsystems by using a filter. A filtering-based multi-innovation stochastic gradient algorithm is presented, whose parameter estimation accuracy is higher than the multi-innovation stochastic gradient algorithm. The simulation results confirm that these two algorithms are effective.
ISSN:1751-8644
1751-8652
DOI:10.1049/iet-cta.2018.6132