2017
Journal article  Open Access

Bayesian Volterra system identification using reversible jump MCMC algorithm

Karakus O., Kuruoglu E. E., Altinkaya M. A.

Bayesian Networks  Electrical and Electronic Engineering  Volterra system identification  Channel estimation  Computer Vision and Pattern Recognition  Nonlinear channel estimation  Nonlinearity degree estimation  Software  Reversible jump MCMC  Signal Processing  Control and Systems Engineering 

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.

Source: Signal processing (Print) 141 (2017): 125–136. doi:10.1016/j.sigpro.2017.05.031

Publisher: Elsevier, Amsterdam , Paesi Bassi


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BibTeX entry
@article{oai:it.cnr:prodotti:373377,
	title = {Bayesian Volterra system identification using reversible jump MCMC algorithm},
	author = {Karakus O. and Kuruoglu E. E. and Altinkaya M. A.},
	publisher = {Elsevier, Amsterdam , Paesi Bassi},
	doi = {10.1016/j.sigpro.2017.05.031},
	journal = {Signal processing (Print)},
	volume = {141},
	pages = {125–136},
	year = {2017}
}