System identification of Wiener systems with B-spline functions using De Boor recursion
System identification of Wiener systems with B-spline functions using De Boor recursion
In this article a simple and effective algorithm is introduced for the system identification of the Wiener system using observational input/output data. The nonlinear static function in the Wiener system is modelled using a B-spline neural network. The Gauss–Newton algorithm is combined with De Boor algorithm (both curve and the first order derivatives) for the parameter estimation of the Wiener model, together with the use of a parameter initialisation scheme. Numerical examples are utilised to demonstrate the efficacy of the proposed approach.
b-spline, de boor recursion, wiener system, system identification
1666-1674
Hong, Xia
e6551bb3-fbc0-4990-935e-43b706d8c679
Mitchell, Richard J.
9f3d6d28-98ab-4a4e-9907-0d1895700281
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
September 2013
Hong, Xia
e6551bb3-fbc0-4990-935e-43b706d8c679
Mitchell, Richard J.
9f3d6d28-98ab-4a4e-9907-0d1895700281
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Hong, Xia, Mitchell, Richard J. and Chen, Sheng
(2013)
System identification of Wiener systems with B-spline functions using De Boor recursion.
International Journal of Systems Science, 44 (9), .
(doi:10.1080/00207721.2012.669863).
Abstract
In this article a simple and effective algorithm is introduced for the system identification of the Wiener system using observational input/output data. The nonlinear static function in the Wiener system is modelled using a B-spline neural network. The Gauss–Newton algorithm is combined with De Boor algorithm (both curve and the first order derivatives) for the parameter estimation of the Wiener model, together with the use of a parameter initialisation scheme. Numerical examples are utilised to demonstrate the efficacy of the proposed approach.
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e-pub ahead of print date: 26 March 2012
Published date: September 2013
Keywords:
b-spline, de boor recursion, wiener system, system identification
Organisations:
Southampton Wireless Group
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Local EPrints ID: 353150
URI: http://eprints.soton.ac.uk/id/eprint/353150
ISSN: 0020-7721
PURE UUID: 2da64a90-018e-4116-b183-41c23c12fd8b
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Date deposited: 03 Jun 2013 11:03
Last modified: 14 Mar 2024 14:02
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Author:
Xia Hong
Author:
Richard J. Mitchell
Author:
Sheng Chen
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