Recursive Identification Methods for Multivariate Output-error Moving Average Systems Using the Auxiliary Model

Qinyao Liu, Feng Ding*, Ahmed Alsaedi, Tasawar Hayat

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


This paper studies the parameter identification problems of multivariate output-error moving average systems. An auxiliary model based extended stochastic gradient algorithm and based recursive extended least squares algorithm are proposed for estimating the parameters of the multivariate output-error moving average systems. By using the multi-innovation identification theory, an auxiliary model based multi-innovation extended stochastic gradient algorithm is derived for improving the parameter estimation accuracy. Finally, the simulation results indicate that the proposed algorithms can work well.

Original languageEnglish
Pages (from-to)1070-1079
Number of pages10
JournalInternational Journal of Control, Automation and Systems
Issue number3
Publication statusPublished - 1 Jun 2018
Externally publishedYes


  • Auxiliary model
  • multivariate system
  • parameter estimation
  • recursive identification

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