Computational system identification of continuous-time nonlinear systems using approximate Bayesian computation

Krishnanathan, K., Anderson, S.R., Billings, S.A. et al. (1 more author) (2015) Computational system identification of continuous-time nonlinear systems using approximate Bayesian computation. International Journal of Systems Science. ISSN 0020-7721

Abstract

Metadata

Authors/Creators:
  • Krishnanathan, K.
  • Anderson, S.R.
  • Billings, S.A.
  • Kadirkamanathan, V.
Copyright, Publisher and Additional Information: © 2015 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: Models; NARMAX; continuous-time systems; system identification and signal processing; Bayesian estimation; computational system identification; nonlinear; approximate Bayesian computation
Dates:
  • Published: 12 October 2015
  • Accepted: 1 September 2015
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: 21 Jan 2016 14:18
Last Modified: 30 Oct 2016 00:47
Published Version: http://dx.doi.org/10.1080/00207721.2015.1090643
Status: Published
Publisher: Taylor & Francis
Refereed: Yes
Identification Number: https://doi.org/10.1080/00207721.2015.1090643

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Filename: Krishnanathan_ABC_IJSS.pdf

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