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Identification of hysteretic systems using NARX models, part II : a Bayesian approach

Worden, K. and Barthorpe, R. J. and Hensman, J. J. (2012) Identification of hysteretic systems using NARX models, part II : a Bayesian approach. In: Conference Proceedings of the Society for Experimental Mechanics Series :. , pp. 57-65. ISBN 9781461424307

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Following on from the first part of this short sequence, this paper will investigate the use of a Bayesian methodology for the identification of Bouc-Wen hysteretic systems by NARX models. The approach - based on Markov Chain Monte Carlo - offers a number of advantages over the evolutionary approach of the first paper. Among them are the ability to sample from the probability density functions of the parameters in order to develop nonparametric estimators and the possibility of selecting model terms in a principled manner. The paper will investigate the use of the Deviance Information Criterion (DIC) as a means of selecting model terms, specifically the special basis functions developed for the Bouc-Wen system in Part I. Results for simulated data will be given.

Item Type: Contribution in Book/Report/Proceedings
Uncontrolled Keywords: Bayesian inference ; Hysteresis ; Markov chain monte carlo (MCMC) ; Nonlinear system identification ; The bouc-wen model
Departments: Faculty of Health and Medicine > Medicine
ID Code: 85092
Deposited By: ep_importer_pure
Deposited On: 07 Mar 2017 11:18
Refereed?: Yes
Published?: Published
Last Modified: 04 Dec 2018 10:32
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