Maximum likelihood based multi-innovation stochastic gradient estimation for controlled autoregressive ARMA systems using the data filtering technique

Feiyan Chen, Feng Ding

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

1 Citation (Scopus)

Abstract

This paper considers parameter estimation problems of a controlled autoregressive ARMA system. We decompose this system into two subsystems, use the data filtering technique to derive a maximum likelihood multi-innovation stochastic gradient algorithm. The simulation results show that the proposed algorithm has a higher computational efficiency than the maximum likelihood gradient algorithm and the filtering-based maximum likelihood stochastic gradient algorithm.

Original languageEnglish
Title of host publicationProceeding of the 11th World Congress on Intelligent Control and Automation, WCICA 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5993-5998
Number of pages6
EditionMarch
ISBN (Electronic)9781479958252
DOIs
Publication statusPublished - 2 Mar 2015
Externally publishedYes
Event2014 11th World Congress on Intelligent Control and Automation, WCICA 2014 - Shenyang, China
Duration: 29 Jun 20144 Jul 2014

Publication series

NameProceedings of the World Congress on Intelligent Control and Automation (WCICA)
NumberMarch
Volume2015-March

Conference

Conference2014 11th World Congress on Intelligent Control and Automation, WCICA 2014
Country/TerritoryChina
CityShenyang
Period29/06/144/07/14

Keywords

  • Filtering
  • Maximum likelihood
  • Parameter estimation
  • Stochastic gradient

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