Hierarchical multi-innovation generalised extended stochastic gradient methods for multivariable equation-error autoregressive moving average systems

This study presents the modelling technology of multivariable equation-error autoregressive moving average (EEARMA) systems through observational data of systems. Aiming to develop a simplified identification algorithm, the original multivariable EEARMA model to be identified is separated into two s...

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Bibliographic Details
Published in:IET control theory & applications Vol. 14; no. 10; pp. 1276 - 1286
Main Authors: Xu, Ling, Ding, Feng, Lu, Xian, Wan, Lijuan, Sheng, Jie
Format: Journal Article
Language:English
Published: The Institution of Engineering and Technology 02.07.2020
Subjects:
autoregressive moving average processes
gradient methods
identification
innovation matrices
model decomposition
multivariable EEARMA model
multivariable EEARMA systems
multivariable equation‐error autoregressive moving average systems
Research Article
stochastic processes
sub‐identification models
two‐stage generalised extended stochastic gradient algorithm
two‐stage GESG algorithm
two‐stage MI‐GESG algorithm
two‐stage multiinnovation gradient methods
two‐stage multiple innovation GESG algorithm
sub-identification models
multivariable EEARMA model
two-stage generalised extended stochastic gradient algorithm
multivariable EEARMA systems
autoregressive moving average processes
two-stage multiple innovation GESG algorithm
identification
model decomposition
two-stage MI-GESG algorithm
two-stage multiinnovation gradient methods
multivariable equation-error autoregressive moving average systems
innovation matrices
stochastic processes
gradient methods
two-stage GESG algorithm
ISSN:1751-8644, 1751-8652
Online Access:Get full text
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Summary:This study presents the modelling technology of multivariable equation-error autoregressive moving average (EEARMA) systems through observational data of systems. Aiming to develop a simplified identification algorithm, the original multivariable EEARMA model to be identified is separated into two sub-identification models. After the model decomposition, a two-stage generalised extended stochastic gradient (GESG) algorithm is presented in accordance with these two separated submodels. By adding more observations to the recursive computation, the corresponding two-stage multi-innovation GESG (MI-GESG) algorithm, namely, hierarchical multi-innovation generalised extended stochastic gradient algorithm, is derived for the multivariable EEARMA systems through expanding the innovation vector to the innovation matrices. The simulation example verifies that the performance about the computational accuracy of the two-stage MI-GESG algorithm is improved compared with the two-stage GESG algorithm.
ISSN:1751-8644
1751-8652
DOI:10.1049/iet-cta.2019.0731