Bayesian system identification of dynamical systems using highly informative training data

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

Metadata

Item Type: Article
Authors/Creators:
  • Green, P.L.
  • Cross, E.J.
  • Worden, K.
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:
  • Published: May 2015
  • Published (online): 4 November 2014
  • Accepted: 9 October 2014
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):

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