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
Abstract
Modeling uncertainty has been an active and important topic in the fields of data-driven modeling and machine learning. Uncertainty ubiquitously exists in any data modeling process, making it challenging to identify the optimal models among many potential candidates. This article proposes an uncertainty-informed method to address the model selection problem. The performance of the proposed method is evaluated on a dataset generated from a complex system model. The experimental results demonstrate the effectiveness of the proposed method and its superiority over conventional approaches. This method has minimal requirements for the length of training data and model types, making it applicable for various modeling frameworks.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 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: |
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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: | Funder 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 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:214151 |
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Filename: MED2024 Uncertainty-infomred model selection (Final Accepted Manuscript).pdf
Licence: CC-BY 4.0