Green, PL and Worden, K
(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).
20140405-.
This is the latest version of this item.
Text
PHIL_TRANS_A_2015.pdf - Unspecified Download (1MB) |
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.
Item Type: | Article |
---|---|
Uncontrolled Keywords: | nonlinear, system identification, model updating, Bayesian |
Depositing User: | Symplectic Admin |
Date Deposited: | 18 Sep 2015 09:03 |
Last Modified: | 15 Dec 2022 16:08 |
DOI: | 10.1098/rsta.2014.0405 |
Related URLs: | |
URI: | https://livrepository.liverpool.ac.uk/id/eprint/2026580 |
Available Versions of this Item
- Bayesian and Markov chain Monte Carlo methods for identifying nonlinear systems in the presence of uncertainty. (deposited 18 Sep 2015 09:03) [Currently Displayed]