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Recursive and iterative stochastic gradient algorithms based on two-step update estimation for wiener systems

  • Harbin Institute of Technology Shenzhen

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

Abstract

In this paper, a recursive stochastic gradient algorithm based on two-step update estimation and an iterative stochastic gradient algorithm based on two-step update estimation are established for the Wiener system by introducing a relaxation factor, which controls the relative importance of the two estimation parts. In addition, the convergence performance of the proposed SG-TSU and ISG-TSU algorithms are then analyzed. It is shown by a numerical example that if the relaxation factor is appropriately chosen, the proposed SG-TSU and ISG-TSU algorithms converge more quickly and have higher convergence precision than the standard SG and ISG algorithms, respectively.

Original languageEnglish
Title of host publication2017 Asian Control Conference, ASCC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1940-1945
Number of pages6
ISBN (Electronic)9781509015733
DOIs
StatePublished - 7 Feb 2018
Externally publishedYes
Event2017 11th Asian Control Conference, ASCC 2017 - Gold Coast, Australia
Duration: 17 Dec 201720 Dec 2017

Publication series

Name2017 Asian Control Conference, ASCC 2017
Volume2018-January

Conference

Conference2017 11th Asian Control Conference, ASCC 2017
Country/TerritoryAustralia
CityGold Coast
Period17/12/1720/12/17

Keywords

  • Wiener system
  • identification algorithm
  • stochastic gradient
  • two-step update estimation

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