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dc.contributor.authorKarakuş, Oktay
dc.contributor.authorKuruoğlu, Ercan Engin
dc.contributor.authorAltınkaya, Mustafa Aziz
dc.date.accessioned2018-01-08T11:03:22Z
dc.date.available2018-01-08T11:03:22Z
dc.date.issued2017-12
dc.identifier.citationKarakuş, O., Kuruoğlu, E. E., and Altınkaya, M. A. (2017). Bayesian Volterra system identification using reversible jump MCMC algorithm. Signal Processing, 141, 125-136. doi:10.1016/j.sigpro.2017.05.031en_US
dc.identifier.issn0165-1684
dc.identifier.urihttp://doi.org/10.1016/j.sigpro.2017.05.031
dc.identifier.urihttp://hdl.handle.net/11147/6656
dc.description.abstractVolterra systems have had significant success in modelling nonlinear systems in various real-world applications. However, it is generally assumed that the nonlinearity degree of the system is known beforehand. In this paper, we contribute to the literature on Volterra system identification (VSI) with a numerical Bayesian approach which identifies model coefficients and the nonlinearity degree concurrently. Although this numerical Bayesian method, namely reversible jump Markov chain Monte Carlo (RJMCMC) algorithm has been used with success in various model selection problems, our use is in a novel context in the sense that both memory size and nonlinearity degree are estimated. The aforementioned study ensures an anomalous approach to RJMCMC and provides a new understanding on its flexible use which enables trans-structural transitions between different classes of models in addition to transdimensional transitions for which it is classically used. We study the performance of the method on synthetically generated data including OFDM communications over a nonlinear channel.en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.relation.isversionof10.1016/j.sigpro.2017.05.031en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectChannel estimationen_US
dc.subjectNonlinearity degree estimationen_US
dc.subjectReversible jump MCMCen_US
dc.subjectVolterra system identificationen_US
dc.subjectBayesian Networksen_US
dc.titleBayesian Volterra system identification using reversible jump MCMC algorithmen_US
dc.typearticleen_US
dc.contributor.authorIDTR179468en_US
dc.contributor.authorIDTR114046en_US
dc.contributor.institutionauthorKarakuş, Oktay
dc.contributor.institutionauthorAltınkaya, Mustafa Aziz
dc.relation.journalSignal Processingen_US
dc.contributor.departmentİYTE, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.identifier.volume141en_US
dc.identifier.startpage125en_US
dc.identifier.endpage136en_US
dc.identifier.wosWOS:000406987500011
dc.identifier.scopusSCOPUS:2-s2.0-85020312846
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US


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