Identification of continuous-time models for nonlinear dynamic systems from discrete data

Guo, Y., Guo, L.Z., Billings, S.A. et al. (1 more author) (2016) Identification of continuous-time models for nonlinear dynamic systems from discrete data. INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE, 47 (12). pp. 3044-3054. ISSN 0020-7721

Abstract

Metadata

Authors/Creators:
  • Guo, Y.
  • Guo, L.Z.
  • Billings, S.A.
  • Wei, H-L.
Copyright, Publisher and Additional Information: © 2016 Taylor & Francis. This is an author produced version of a paper subsequently published in International Journal of Systems Science. Uploaded in accordance with the publisher's self-archiving policy.
Keywords: modulating function method; nonlinear system identification; continuous-time model; iOFR algorithm; orthogonal forward regression
Dates:
  • Published (online): 27 July 2016
  • Accepted: 2 July 2016
  • Published: 27 July 2016
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
EUROPEAN COMMISSION - HORIZON 2020PROGRESS - 637302
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: 01 Aug 2016 15:14
Last Modified: 06 Aug 2017 13:30
Published Version: http://dx.doi.org/10.1080/00207721.2015.1069906
Status: Published
Publisher: Taylor & Francis
Refereed: Yes
Identification Number: https://doi.org/10.1080/00207721.2015.1069906
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