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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