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Latest-Estimation Based Hierarchical Recursive Extended Least Squares algorithm for ARMAX model

  • Harbin Institute of Technology Shenzhen

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

Abstract

For ARMAX models, a Latest-Estimation Based Hierarchical Recursive Extended Least Squares algorithm is presented in this paper. The basic idea is to make full use of the latest estimation, and combine this with the hierarchical idea. In the proposed algorithm, the estimates of the white noise information vector is updated by using the latest estimation. The convergence performance of the proposed LE-HRELS algorithm is simply analyzed. It is shown by a numerical example that the LE-HRELS algorithm possesses faster convergence speed and higher convergence precision compared with the standard RELS and the HRELS algorithm.

Original languageEnglish
Title of host publicationProceedings of the 35th Chinese Control Conference, CCC 2016
EditorsJie Chen, Qianchuan Zhao, Jie Chen
PublisherIEEE Computer Society
Pages273-278
Number of pages6
ISBN (Electronic)9789881563910
DOIs
StatePublished - 26 Aug 2016
Externally publishedYes
Event35th Chinese Control Conference, CCC 2016 - Chengdu, China
Duration: 27 Jul 201629 Jul 2016

Publication series

NameChinese Control Conference, CCC
Volume2016-August
ISSN (Print)1934-1768
ISSN (Electronic)2161-2927

Conference

Conference35th Chinese Control Conference, CCC 2016
Country/TerritoryChina
CityChengdu
Period27/07/1629/07/16

Keywords

  • ARMAX model
  • latest estimation
  • system identification

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