Please use this identifier to cite or link to this item:
Title: Bayesian Volterra system identification using reversible jump MCMC algorithm
Authors: Karakuş, Oktay
Kuruoğlu, Ercan Engin
Altınkaya, Mustafa Aziz
Karakuş, Oktay
Altınkaya, Mustafa Aziz
Izmir Institute of Technology. Electronics and Communication Engineering
Keywords: Channel estimation
Nonlinearity degree estimation
Reversible jump MCMC
Volterra system identification
Bayesian Networks
Issue Date: Dec-2017
Publisher: Elsevier Ltd.
Source: 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
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.
ISSN: 0165-1684
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

Files in This Item:
File Description SizeFormat 
6656.pdfMakale1.5 MBAdobe PDFThumbnail
Show full item record

CORE Recommender


checked on Jul 24, 2021


checked on Jul 24, 2021

Page view(s)

checked on Jul 30, 2021


checked on Jul 30, 2021

Google ScholarTM



Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.