Author： Hong X. Mitchell R.J. Chen S.
Publisher： Taylor & Francis Ltd
ISSN： 1464-5319
Source： International Journal of Systems Science, Vol.44, Iss.9, 2013-09, pp. : 1666-1674
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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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