Green, P.L. and Worden, K. orcid.org/0000-0002-1035-238X (2015) Bayesian and Markov chain Monte Carlo methods for identifying nonlinear systems in the presence of uncertainty. Philosophical Transactions Of The Royal Society A - Mathematical Physical And Engineering Sciences, 373 (2051). ARTN 20140405. ISSN 1364-503X
Abstract
In this paper, the authors outline the general principles behind an approach to Bayesian system identification and highlight the benefits of adopting a Bayesian framework when attempting to identify models of nonlinear dynamical systems in the presence of uncertainty. It is then described how, through a summary of some key algorithms, many of the potential difficulties associated with a Bayesian approach can be overcome through the use of Markov chain Monte Carlo (MCMC) methods. The paper concludes with a case study, where an MCMC algorithm is used to facilitate the Bayesian system identification of a nonlinear dynamical system from experimentally observed acceleration time histories.
Metadata
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Copyright, Publisher and Additional Information: | © 2015 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited. | ||||
Keywords: | nonlinear; system identification; model updating; Bayesian | ||||
Dates: |
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Institution: | The University of Sheffield | ||||
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Mechanical Engineering (Sheffield) | ||||
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Depositing User: | Symplectic Sheffield | ||||
Date Deposited: | 08 Aug 2016 15:04 | ||||
Last Modified: | 23 Jun 2023 22:04 | ||||
Published Version: | http://dx.doi.org/10.1098/rsta.2014.0405 | ||||
Status: | Published | ||||
Publisher: | Royal Society | ||||
Refereed: | Yes | ||||
Identification Number: | https://doi.org/10.1098/rsta.2014.0405 | ||||
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