Frequency-domain least-squares support vector machines to deal with correlated errors when identifying linear time-varying systems

J. Lataire, D. Piga, R. Toth

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

5 Citations (Scopus)

Abstract

A Least-Squares Support Vector Machine (LS-SVM) estimator, formulated in the frequency domain is proposed to identify linear time-varying dynamic systems. The LS-SVM aims at learning the structure of the time variation in a data driven way. The frequency domain is chosen for its superior robustness w.r.t. correlated errors for the calibration of the hyper parameters of the model. The time-domain and the frequency-domain implementations are compared on a simulation example to show the effectiveness of the proposed approach. It is demonstrated that the time- domain formulation is mislead during the calibration due to the fact that the noise on the estimation and calibration data sets are correlated. This is not the case for the frequency-domain implementation.
Original languageEnglish
Title of host publicationProceedings of the 19th IFAC World Congress of the International Federation of Automatic Control, (IFAC'14), 24-29 August 2014, Cape Town, South Africa
Pages10024-10029
Publication statusPublished - 2014
Event19th World Congress of the International Federation of Automatic Control (IFAC 2014 World Congress) - Cape Town International Convention Centre, Cape Town, South Africa
Duration: 24 Aug 201429 Aug 2014
Conference number: 19
http://www.ifac2014.org

Conference

Conference19th World Congress of the International Federation of Automatic Control (IFAC 2014 World Congress)
Abbreviated titleIFAC 2014
Country/TerritorySouth Africa
CityCape Town
Period24/08/1429/08/14
Internet address

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