The filtering based auxiliary model generalized extended stochastic gradient identification for a multivariate output-error system with autoregressive moving average noise using the multi-innovation theory

Feng Ding*, Lijuan Wan, Yunze Guo, Feiyan Chen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)

Abstract

This paper studies the parameter estimation algorithms of multivariate output-error autoregressive moving average (M-OEARMA) systems. By means of the filtering technique and the auxiliary model identification idea, this paper gives an auxiliary model generalized extended stochastic gradient (AM-GESG) algorithm for identifying the M-OEARMA system as a comparison. In order to enhance the performance of the AM-GESG algorithm, a modified filtering based AM-GESG algorithm and a filtering based auxiliary model multi-innovation generalized extended stochastic gradient algorithm are proposed. Compared with the AM-GESG algorithm, the proposed two algorithms can generate highly accurate parameter estimates. The simulation examples demonstrate that the proposed algorithms are effective for identifying the M-OEARMA systems.

Original languageEnglish
Pages (from-to)5591-5609
Number of pages19
JournalJournal of the Franklin Institute
Volume357
Issue number9
DOIs
Publication statusPublished - Jun 2020

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