Guo, Y, Guo, LZ, Billings, SA et al. (1 more author) (2016) Ultra-Orthogonal Forward Regression Algorithms for the Identification of Non-Linear Dynamic Systems. Neurocomputing, 173 (3). pp. 715-723. ISSN 0925-2312
Abstract
A new ultra-least squares (ULS) criterion is introduced for system identification. Unlike the standard least squares criterion which is based on the Euclidean norm of the residuals, the new ULS criterion is derived from the Sobolev space norm. The new criterion measures not only the discrepancy between the observed signals and the model prediction but also the discrepancy between the associated weak derivatives of the observed and the model signals. The new ULS criterion possesses a clear physical interpretation and is easy to implement. Based on this, a new Ultra-Orthogonal Forward Regression (UOFR) algorithm is introduced for nonlinear system identification, which includes converting a least squares regression problem into the associated ultra-least squares problem and solving the ultra-least squares problem using the orthogonal forward regression method. Numerical simulations show that the new UOFR algorithm can significantly improve the performance of the classic OFR algorithm.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2015 Elsevier. This is an author produced version of a paper subsequently published in Neurocomputing. 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: | Orthogonal forward regression; System identification; Ultra-least squares; Ultra-Orthogonal Forward Regression; Ultra-Orthogonal Least Squares |
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 ALZHEIMERS RESEARCH UK ARUK-PPG2014B-25 ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL (EPSRC) EP/I011056/1 ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL (EPSRC) EP/H00453X/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 16 Nov 2016 12:28 |
Last Modified: | 22 Aug 2017 16:38 |
Published Version: | http://dx.doi.org/10.1016/j.neucom.2015.08.022 |
Status: | Published |
Publisher: | Elsevier |
Refereed: | Yes |
Identification Number: | 10.1016/j.neucom.2015.08.022 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:107310 |