Lifelong learning meets dynamic processes: an emerging streaming process prediction framework with delayed process output measurement

Liu, T., Chen, S., Yang, P. orcid.org/0000-0002-8553-7127 et al. (3 more authors) (2024) Lifelong learning meets dynamic processes: an emerging streaming process prediction framework with delayed process output measurement. IEEE Transactions on Control Systems Technology, 32 (2). pp. 384-398. ISSN 1063-6536

Abstract

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Item Type: Article
Authors/Creators:
Copyright, Publisher and Additional Information:

© 2023 The Authors. Except as otherwise noted, this author-accepted version of a journal article published in IEEE Transactions on Control Systems Technology is made available via the University of Sheffield Research Publications and Copyright Policy under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/

Keywords: Lifelong learning; dynamic industrial processes; delayed output measurement; process drifts, knowledge transfer
Dates:
  • Published: March 2024
  • Published (online): 2 October 2023
  • Accepted: 29 August 2023
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield)
Funding Information:
Funder
Grant number
INNOVATE UK
TS/V002953/1
Depositing User: Symplectic Sheffield
Date Deposited: 07 Sep 2023 10:45
Last Modified: 28 Oct 2024 16:31
Status: Published
Publisher: Institute of Electrical and Electronics Engineers
Refereed: Yes
Identification Number: 10.1109/TCST.2023.3312850
Open Archives Initiative ID (OAI ID):

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