Please use this identifier to cite or link to this item:
https://hdl.handle.net/11147/6656
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Karakuş, Oktay | - |
dc.contributor.author | Kuruoğlu, Ercan Engin | - |
dc.contributor.author | Altınkaya, Mustafa Aziz | - |
dc.date.accessioned | 2018-01-08T11:03:22Z | |
dc.date.available | 2018-01-08T11:03:22Z | |
dc.date.issued | 2017-12 | |
dc.identifier.citation | Karakuş, 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.2017.05.031 | en_US |
dc.identifier.issn | 0165-1684 | |
dc.identifier.issn | 0165-1684 | - |
dc.identifier.uri | http://doi.org/10.1016/j.sigpro.2017.05.031 | |
dc.identifier.uri | http://hdl.handle.net/11147/6656 | |
dc.description.abstract | Volterra 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.iso | en | en_US |
dc.publisher | Elsevier Ltd. | en_US |
dc.relation.ispartof | Signal Processing | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | Channel estimation | en_US |
dc.subject | Nonlinearity degree estimation | en_US |
dc.subject | Reversible jump MCMC | en_US |
dc.subject | Volterra system identification | en_US |
dc.subject | Bayesian Networks | en_US |
dc.title | Bayesian Volterra system identification using reversible jump MCMC algorithm | en_US |
dc.type | Article | en_US |
dc.authorid | TR179468 | en_US |
dc.authorid | TR114046 | en_US |
dc.institutionauthor | Karakuş, Oktay | - |
dc.institutionauthor | Altınkaya, Mustafa Aziz | - |
dc.department | İzmir Institute of Technology. Electrical and Electronics Engineering | en_US |
dc.identifier.volume | 141 | en_US |
dc.identifier.startpage | 125 | en_US |
dc.identifier.endpage | 136 | en_US |
dc.identifier.wos | WOS:000406987500011 | en_US |
dc.identifier.scopus | 2-s2.0-85020312846 | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.identifier.doi | 10.1016/j.sigpro.2017.05.031 | - |
dc.relation.doi | 10.1016/j.sigpro.2017.05.031 | en_US |
dc.coverage.doi | 10.1016/j.sigpro.2017.05.031 | en_US |
dc.identifier.wosquality | Q2 | - |
dc.identifier.scopusquality | N/A | - |
item.languageiso639-1 | en | - |
item.openairetype | Article | - |
item.grantfulltext | open | - |
item.cerifentitytype | Publications | - |
item.fulltext | With Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
crisitem.author.dept | 03.05. Department of Electrical and Electronics Engineering | - |
Appears in Collections: | Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
CORE Recommender
SCOPUSTM
Citations
10
checked on Sep 1, 2024
WEB OF SCIENCETM
Citations
10
checked on Aug 24, 2024
Page view(s)
718
checked on Sep 2, 2024
Download(s)
380
checked on Sep 2, 2024
Google ScholarTM
Check
Altmetric
Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.