Recursive Identification Methods for Multivariate Output-error Moving Average Systems Using the Auxiliary Model

Qinyao Liu; Feng Ding; Ahmed Alsaedi; Tasawar Hayat

doi:10.1007/s12555-017-0101-7

Recursive Identification Methods for Multivariate Output-error Moving Average Systems Using the Auxiliary Model

Qinyao Liu
, Feng Ding^*
, Ahmed Alsaedi
, Tasawar Hayat

^*Corresponding author for this work

Jiangnan University
King Abdulaziz University
Quaid-I-Azam University

Research output: Contribution to journal › Article › peer-review

10 Citations (Scopus)

Abstract

This paper studies the parameter identification problems of multivariate output-error moving average systems. An auxiliary model based extended stochastic gradient algorithm and based recursive extended least squares algorithm are proposed for estimating the parameters of the multivariate output-error moving average systems. By using the multi-innovation identification theory, an auxiliary model based multi-innovation extended stochastic gradient algorithm is derived for improving the parameter estimation accuracy. Finally, the simulation results indicate that the proposed algorithms can work well.

Original language	English
Pages (from-to)	1070-1079
Number of pages	10
Journal	International Journal of Control, Automation and Systems
Volume	16
Issue number	3
DOIs	https://doi.org/10.1007/s12555-017-0101-7
Publication status	Published - 1 Jun 2018
Externally published	Yes

Keywords

Auxiliary model
multivariate system
parameter estimation
recursive identification

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10.1007/s12555-017-0101-7

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title = "Recursive Identification Methods for Multivariate Output-error Moving Average Systems Using the Auxiliary Model",

abstract = "This paper studies the parameter identification problems of multivariate output-error moving average systems. An auxiliary model based extended stochastic gradient algorithm and based recursive extended least squares algorithm are proposed for estimating the parameters of the multivariate output-error moving average systems. By using the multi-innovation identification theory, an auxiliary model based multi-innovation extended stochastic gradient algorithm is derived for improving the parameter estimation accuracy. Finally, the simulation results indicate that the proposed algorithms can work well.",

keywords = "Auxiliary model, multivariate system, parameter estimation, recursive identification",

author = "Qinyao Liu and Feng Ding and Ahmed Alsaedi and Tasawar Hayat",

note = "Publisher Copyright: {\textcopyright} 2018, Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature.",

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AU - Liu, Qinyao

AU - Ding, Feng

AU - Alsaedi, Ahmed

AU - Hayat, Tasawar

PY - 2018/6/1

Y1 - 2018/6/1

N2 - This paper studies the parameter identification problems of multivariate output-error moving average systems. An auxiliary model based extended stochastic gradient algorithm and based recursive extended least squares algorithm are proposed for estimating the parameters of the multivariate output-error moving average systems. By using the multi-innovation identification theory, an auxiliary model based multi-innovation extended stochastic gradient algorithm is derived for improving the parameter estimation accuracy. Finally, the simulation results indicate that the proposed algorithms can work well.

AB - This paper studies the parameter identification problems of multivariate output-error moving average systems. An auxiliary model based extended stochastic gradient algorithm and based recursive extended least squares algorithm are proposed for estimating the parameters of the multivariate output-error moving average systems. By using the multi-innovation identification theory, an auxiliary model based multi-innovation extended stochastic gradient algorithm is derived for improving the parameter estimation accuracy. Finally, the simulation results indicate that the proposed algorithms can work well.

KW - Auxiliary model

KW - multivariate system

KW - parameter estimation

KW - recursive identification

UR - https://www.scopus.com/pages/publications/85044731301

U2 - 10.1007/s12555-017-0101-7

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M3 - Article

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JF - International Journal of Control, Automation and Systems

IS - 3

ER -

Recursive Identification Methods for Multivariate Output-error Moving Average Systems Using the Auxiliary Model

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Keywords

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