Uncertainty-informed model selection method for nonlinear system identification and interpretable machine learning

Yuanlin, G. and Wei, H.-L. orcid.org/0000-0002-4704-7346 (2024) Uncertainty-informed model selection method for nonlinear system identification and interpretable machine learning. In: 2024 32nd Mediterranean Conference on Control and Automation (MED). 2024 32nd Mediterranean Conference on Control and Automation (MED), 11-14 Jun 2024, Chania, Crete, Greece. Institute of Electrical and Electronics Engineers (IEEE) , pp. 909-914. ISBN 9798350395457

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Item Type: Proceedings Paper
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© 2024 The Authors. Except as otherwise noted, this author-accepted version of a paper published in 2024 32nd Mediterranean Conference on Control and Automation (MED) 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: Data-driven modeling; Adaptation models; Uncertainty; Recurrent neural networks; Training data; Machine learning; Data models
Dates:
  • Published: 27 June 2024
  • Published (online): 27 June 2024
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield)
Funding Information:
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Grant number
SCIENCE AND TECHNOLOGY FACILITIES COUNCIL
ST/Y001524/1
NATURAL ENVIRONMENT RESEARCH COUNCIL
NE/V002511/1
Depositing User: Symplectic Sheffield
Date Deposited: 01 Jul 2024 15:22
Last Modified: 01 Jul 2024 22:35
Status: Published
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Refereed: Yes
Identification Number: 10.1109/MED61351.2024.10566184
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