On the application of Gaussian process latent force models for joint input-state-parameter estimation : With a view to Bayesian operational identification

Rogers, T.J. orcid.org/0000-0002-3433-3247, Worden, K. and Cross, E.J. (2020) On the application of Gaussian process latent force models for joint input-state-parameter estimation : With a view to Bayesian operational identification. Mechanical Systems and Signal Processing, 140. 106580. ISSN 0888-3270

Abstract

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Authors/Creators:
Copyright, Publisher and Additional Information: © 2020 Elsevier Ltd. This is an author produced version of a paper subsequently published in Mechanical Systems and Signal Processing. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords: Bayesian; System Identification; Operational Modal Analysis; Gaussian Process; Latent Force Model
Dates:
  • Accepted: 16 December 2019
  • Published (online): 28 February 2020
  • Published: June 2020
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Mechanical Engineering (Sheffield)
Funding Information:
FunderGrant number
Engineering and Physical Science Research CouncilEP/R003645/1; EP/S001565/1
Depositing User: Symplectic Sheffield
Date Deposited: 18 Feb 2020 10:48
Last Modified: 12 Mar 2020 16:51
Status: Published
Publisher: Elsevier
Refereed: Yes
Identification Number: https://doi.org/10.1016/j.ymssp.2019.106580

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Filename: mssp19_1540_accepted_manuscript.pdf

Licence: CC-BY-NC-ND 4.0

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