RBF neural network controller based on OLSSVR

Kemal Ucak, Gulay Oke

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

1 Citation (Scopus)

Abstract

In this paper, a predictive adaptation method based on Online Least Square Support Vector Regression (OLSVR) for a RBF controller has been proposed. System Jacobian is approximated via Online LSSVR model of the system to tune RBF controller. The parameters of the controller have been tuned depending on K-step ahead future behavior of the system to provide adaptation ability to the controller under changing conditions. Levenberg Marquard algorithm is utilized as learning algorithm for controller parameters. The proposed method has been evaluated by simulations carried out on a magnetic levitation system, and the results show that the control performance has been improved.

Original languageEnglish
Title of host publication2013 9th Asian Control Conference, ASCC 2013
DOIs
Publication statusPublished - 2013
Event2013 9th Asian Control Conference, ASCC 2013 - Istanbul, Turkey
Duration: 23 Jun 201326 Jun 2013

Publication series

Name2013 9th Asian Control Conference, ASCC 2013

Conference

Conference2013 9th Asian Control Conference, ASCC 2013
Country/TerritoryTurkey
CityIstanbul
Period23/06/1326/06/13

Keywords

  • Levenberg Marquard
  • Magnetic Levitation
  • Online Support Vector Regression
  • RBF Neural Network Controller

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