Ultra-Orthogonal Forward Regression Algorithms for the Identification of Non-Linear Dynamic Systems

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

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Authors/Creators:
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:
  • Published: 15 January 2016
  • Accepted: 3 August 2015
  • Published (online): 18 August 2015
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:
FunderGrant number
ALZHEIMERS RESEARCH UKARUK-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: https://doi.org/10.1016/j.neucom.2015.08.022

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