Parameter estimation algorithm for multivariable controlled autoregressive autoregressive moving average systems

Qinyao Liu; Feng Ding; Erfu Yang

doi:10.1016/j.dsp.2018.09.010

Parameter estimation algorithm for multivariable controlled autoregressive autoregressive moving average systems

Qinyao Liu, Feng Ding^*, Erfu Yang

^*Corresponding author for this work

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

18 Citations (Scopus)

Abstract

This paper investigates parameter estimation problems for multivariable controlled autoregressive autoregressive moving average (M-CARARMA) systems. In order to improve the performance of the standard multivariable generalized extended stochastic gradient (M-GESG) algorithm, we derive a partially coupled generalized extended stochastic gradient algorithm by using the auxiliary model. In particular, we divide the identification model into several subsystems based on the hierarchical identification principle and estimate the parameters using the coupled relationship between these subsystems. The simulation results show that the new algorithm can give more accurate parameter estimates of the M-CARARMA system than the M-GESG algorithm.

Original language	English
Pages (from-to)	323-331
Number of pages	9
Journal	Digital Signal Processing: A Review Journal
Volume	83
DOIs	https://doi.org/10.1016/j.dsp.2018.09.010
Publication status	Published - Dec 2018
Externally published	Yes

Keywords

Auxiliary model
Coupling identification
Multivariable system
Parameter estimation
Stochastic gradient

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10.1016/j.dsp.2018.09.010

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title = "Parameter estimation algorithm for multivariable controlled autoregressive autoregressive moving average systems",

abstract = "This paper investigates parameter estimation problems for multivariable controlled autoregressive autoregressive moving average (M-CARARMA) systems. In order to improve the performance of the standard multivariable generalized extended stochastic gradient (M-GESG) algorithm, we derive a partially coupled generalized extended stochastic gradient algorithm by using the auxiliary model. In particular, we divide the identification model into several subsystems based on the hierarchical identification principle and estimate the parameters using the coupled relationship between these subsystems. The simulation results show that the new algorithm can give more accurate parameter estimates of the M-CARARMA system than the M-GESG algorithm.",

keywords = "Auxiliary model, Coupling identification, Multivariable system, Parameter estimation, Stochastic gradient",

author = "Qinyao Liu and Feng Ding and Erfu Yang",

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year = "2018",

month = dec,

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T1 - Parameter estimation algorithm for multivariable controlled autoregressive autoregressive moving average systems

AU - Liu, Qinyao

AU - Ding, Feng

AU - Yang, Erfu

PY - 2018/12

Y1 - 2018/12

N2 - This paper investigates parameter estimation problems for multivariable controlled autoregressive autoregressive moving average (M-CARARMA) systems. In order to improve the performance of the standard multivariable generalized extended stochastic gradient (M-GESG) algorithm, we derive a partially coupled generalized extended stochastic gradient algorithm by using the auxiliary model. In particular, we divide the identification model into several subsystems based on the hierarchical identification principle and estimate the parameters using the coupled relationship between these subsystems. The simulation results show that the new algorithm can give more accurate parameter estimates of the M-CARARMA system than the M-GESG algorithm.

AB - This paper investigates parameter estimation problems for multivariable controlled autoregressive autoregressive moving average (M-CARARMA) systems. In order to improve the performance of the standard multivariable generalized extended stochastic gradient (M-GESG) algorithm, we derive a partially coupled generalized extended stochastic gradient algorithm by using the auxiliary model. In particular, we divide the identification model into several subsystems based on the hierarchical identification principle and estimate the parameters using the coupled relationship between these subsystems. The simulation results show that the new algorithm can give more accurate parameter estimates of the M-CARARMA system than the M-GESG algorithm.

KW - Auxiliary model

KW - Coupling identification

KW - Multivariable system

KW - Parameter estimation

KW - Stochastic gradient

UR - http://www.scopus.com/inward/record.url?scp=85054008103&partnerID=8YFLogxK

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DO - 10.1016/j.dsp.2018.09.010

M3 - Article

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VL - 83

SP - 323

EP - 331

JO - Digital Signal Processing: A Review Journal

JF - Digital Signal Processing: A Review Journal

ER -

Parameter estimation algorithm for multivariable controlled autoregressive autoregressive moving average systems

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Keywords

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