Gu, Y. and Wei, H.L. orcid.org/0000-0002-4704-7346 (2018) A robust model structure selection method for small sample size and multiple datasets problems. Information Sciences, 451-52. pp. 195-209. ISSN 0020-0255
Abstract
In model identification, the existence of uncertainty normally generates negative impact on the accuracy and performance of the identified models, especially when the size of data used is rather small. With a small data set, least squares estimates are biased, the resulting models may not be reliable for further analysis and future use. This study introduces a novel robust model structure selection method for model identification. The proposed method can successfully reduce the model structure uncertainty and therefore improve the model performances. Case studies on simulation data and real data are presented to illustrate how the proposed metric works for robust model identification.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2018 Elsevier. This is an author produced version of a paper subsequently published in Information Sciences. 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: | Nonlinear systems; Systems identification; Model uncertainty; Model structure detection |
Dates: |
|
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: | 03 May 2018 14:37 |
Last Modified: | 07 Apr 2019 00:38 |
Published Version: | https://doi.org/10.1016/j.ins.2018.04.007 |
Status: | Published |
Publisher: | Elsevier |
Refereed: | Yes |
Identification Number: | 10.1016/j.ins.2018.04.007 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:130275 |
Download
Filename: Informaton Sciecnes 2018-451pp209-195.pdf
Licence: CC-BY-NC-ND 4.0