Gu, Y., Wei, H. orcid.org/0000-0002-4704-7346 and Balikhin, M. (2018) Nonlinear predictive model selection and model averaging using information criteria. Systems Science and Control Engineering, 6 (1). pp. 319-328. ISSN 2164-2583
Abstract
This paper is concerned with the model selection and model averaging problems in system identification and data-driven modelling for nonlinear systems. Given a set of data, the objective of model selection is to evaluate a series of candidate models and determine which one best presents the data. Three commonly used criteria, namely, Akaike information criterion (AIC), Bayesian information criterion (BIC) and an adjustable prediction error sum of squares (APRESS) are investigated and their performance in model selection and model averaging is evaluated via a number of case studies using both simulation and real data. The results show that APRESS produces better models in terms of generalization performance and model complexity.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
Keywords: | model selection; model averaging; data-driven modelling; system identification; information criterion |
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) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 06 Jul 2018 14:27 |
Last Modified: | 11 Oct 2018 13:17 |
Published Version: | https://doi.org/10.1080/21642583.2018.1496042 |
Status: | Published |
Publisher: | Taylor & Francis Open |
Refereed: | Yes |
Identification Number: | 10.1080/21642583.2018.1496042 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:133030 |