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dc.contributor.authorKlejmova, Eva
dc.date.accessioned2018-07-10T12:48:17Z
dc.date.available2018-07-10T12:48:17Z
dc.date.issued2016cs
dc.identifier.citationProceedings of the 22nd Conference STUDENT EEICT 2016. s. 375-379. ISBN 978-80-214-5350-0cs
dc.identifier.isbn978-80-214-5350-0
dc.identifier.urihttp://hdl.handle.net/11012/83959
dc.description.abstractAim of this paper is to give recommendation for work with methods used for estimation of coefficients of autoregressive process. We applied Monte Carlo simulations to investigate performance of Burg, Yule-Walker and covariance methods. Evaluation of precision of spectral estimation is done with focus on signal length and lag order. The results are presented in graphical form and briefly discussed. Taking these results into account, Yule-Walker method shows better performance in case of long length signals and in case of overvalued lag order. Burg and covariance methods provide better results in case of short length signal and undervalued lag order.en
dc.formattextcs
dc.format.extent375-379cs
dc.format.mimetypeapplication/pdfen
dc.language.isoencs
dc.publisherVysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.relation.ispartofProceedings of the 22nd Conference STUDENT EEICT 2016en
dc.relation.urihttp://www.feec.vutbr.cz/EEICT/cs
dc.rights© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.subjectAutoregressive processen
dc.subjectAICen
dc.subjectBurg methoden
dc.subjectYule-Walker methoden
dc.subjectcovariance methoden
dc.titleEvaluation of Methods for AR Coefficients Estimation Using Monte Carlo Analysisen
eprints.affiliatedInstitution.departmentFakulta elektrotechniky a komunikačních technologiícs
but.event.date28.04.2016cs
but.event.titleStudent EEICT 2016cs
dc.rights.accessopenAccessen
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen


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