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...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IET control theory & applications Jg. 14; H. 10; S. 1276 - 1286
Hauptverfasser:	Xu, Ling, Ding, Feng, Lu, Xian, Wan, Lijuan, Sheng, Jie
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	The Institution of Engineering and Technology 02.07.2020
Schlagworte:	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-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!