Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/6018
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dc.contributor.authorKarakuş, Oktay
dc.contributor.authorKuruoğlu, Ercan Engin
dc.contributor.authorAltınkaya, Mustafa Aziz
dc.date.accessioned2017-07-25T12:06:09Z
dc.date.available2017-07-25T12:06:09Z
dc.date.issued2016
dc.identifier.citationKarakuş, O., Kuruoğlu, E. E., and Altınkaya, M. A. (2016, August 28 - September 2). Bayesian estimation of polynomial moving average models with unknown degree of nonlinearity. Paper presented at the 24th European Signal Processing Conference, EUSIPCO 2016. doi:10.1109/EUSIPCO.2016.7760507en_US
dc.identifier.isbn9780992862657en_US
dc.identifier.issn2219-5491
dc.identifier.urihttp://doi.org/10.1109/EUSIPCO.2016.7760507
dc.identifier.urihttp://hdl.handle.net/11147/6018
dc.description24th European Signal Processing Conference, EUSIPCO 2016; Hotel Hilton BudapestBudapest; Hungary; 28 August 2016 through 2 September 2016en_US
dc.description.abstractVarious real world phenomena such as optical communication channels, power amplifiers and movement of sea vessels exhibit nonlinear characteristics. The nonlinearity degree of such systems is assumed to be known as a general intention. In this paper, we contribute to the literature with a Bayesian estimation method based on reversible jump Markov chain Monte Carlo (RJMCMC) for polynomial moving average (PMA) models. Our use of RJMCMC is novel and unique in the way of estimating both model memory and the nonlinearity degree. This offers greater flexibility to characterize the models which reflect different nonlinear characters of the measured data. In this study, we aim to demonstrate the potentials of RJMCMC in the identification for PMA models due to its potential of exploring nonlinear spaces of different degrees by sampling.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartof24th European Signal Processing Conference, EUSIPCO 2016en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectNonlinearity degree estimationen_US
dc.subjectPolynomialsen_US
dc.subjectReversible jump MCMCen_US
dc.subjectNonlinear opticsen_US
dc.subjectMarkov processesen_US
dc.subjectMonte Carlo methoden_US
dc.titleBayesian estimation of polynomial moving average models with unknown degree of nonlinearityen_US
dc.typeConference Objecten_US
dc.authoridTR179468en_US
dc.authoridTR114046en_US
dc.contributor.departmentİYTE, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.identifier.volume2016en_US
dc.identifier.startpage1543en_US
dc.identifier.endpage1547en_US
dc.identifier.scopusSCOPUS:2-s2.0-85006043072
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1109/EUSIPCO.2016.7760507-
dc.relation.doi10.1109/EUSIPCO.2016.7760507en_US
dc.coverage.doi10.1109/EUSIPCO.2016.7760507en_US
item.openairetypeConference Object-
item.fulltextWith Fulltext-
item.grantfulltextopen-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
item.languageiso639-1en-
crisitem.author.deptDepartment of Electrical and Electonics Engineering-
Appears in Collections:Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
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