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Latest estimation based recursive stochastic gradient identification algorithms for ARX models

  • Ai Guo Wu
  • , Fang Zhou Fu*
  • , Yu Teng
  • *Corresponding author for this work
  • Harbin Institute of Technology Shenzhen

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

Abstract

A modified recursive stochastic gradient identification algorithm is presented for ARX models. In the presented algorithm, the hierarchical identification principle is first used, and then the unknown true parameters are replaced by their latest estimation. The convergence analysis of the proposed algorithm is given. In addition, a simulation example is employed to show the advantage of the proposed identification algorithms in convergence rates and estimation accuracy compared with some existing algorithms.

Original languageEnglish
Title of host publicationProceedings of the 34th Chinese Control Conference, CCC 2015
EditorsQianchuan Zhao, Shirong Liu
PublisherIEEE Computer Society
Pages2033-2038
Number of pages6
ISBN (Electronic)9789881563897
DOIs
StatePublished - 11 Sep 2015
Externally publishedYes
Event34th Chinese Control Conference, CCC 2015 - Hangzhou, China
Duration: 28 Jul 201530 Jul 2015

Publication series

NameChinese Control Conference, CCC
Volume2015-September
ISSN (Print)1934-1768
ISSN (Electronic)2161-2927

Conference

Conference34th Chinese Control Conference, CCC 2015
Country/TerritoryChina
CityHangzhou
Period28/07/1530/07/15

Keywords

  • Latest estimation
  • hierarchical identification
  • stochastic gradient

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