Green, P.L., Cross, E.J. and Worden, K. (2015) Bayesian system identification of dynamical systems using highly informative training data. Mechanical Systems and Signal Processing, 56-57. 109 - 122. ISSN 0888-3270
Abstract
This paper is concerned with the Bayesian system identification of structural dynamical systems using experimentally obtained training data. It is motivated by situations where, from a large quantity of training data, one must select a subset to infer probabilistic models. To that end, using concepts from information theory, expressions are derived which allow one to approximate the effect that a set of training data will have on parameter uncertainty as well as the plausibility of candidate model structures. The usefulness of this concept is then demonstrated through the system identification of several dynamical systems using both physics-based and emulator models. The result is a rigorous scientific framework which can be used to select 'highly informative' subsets from large quantities of training data.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2014 The Author(s). This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
Keywords: | Bayesian inference; Markov chain Monte Carlo; Nonlinear system identification; Shannon entropy; Tamar bridge |
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) |
Funding Information: | Funder Grant number Engineering and Physical Sciences Research Council EP/K003836/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 03 Mar 2015 15:30 |
Last Modified: | 18 Jan 2021 09:08 |
Status: | Published |
Publisher: | Elsevier |
Refereed: | Yes |
Identification Number: | 10.1016/j.ymssp.2014.10.003 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:83948 |