Name
Title | ||
|---|---|---|
System identification of Wiener systems with B-spline functions using De Boor recursion |
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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1080/00207721.2012.669863 | Int. J. Systems Science |
Keywords | Field | DocType |
parameter initialisation scheme,parameter estimation,de boor recursion,newton algorithm,de boor algorithm,wiener system,system identification,wiener model,b-spline neural network,b-spline function,nonlinear static function,effective algorithm,first order,neural network,spline function,b spline,input output | Wiener process,B-spline,Mathematical optimization,Nonlinear system,De Boor's algorithm,Gaussian process,Estimation theory,Artificial neural network,System identification,Mathematics | Journal |
Volume | Issue | ISSN |
44 | 9 | 0020-7721 |
Citations | PageRank | References |
4 | 0.45 | 15 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| X. Hong | 1 | 157 | 11.12 |
| R. J. Mitchell | 2 | 59 | 3.28 |
| S. Chen | 3 | 4 | 0.45 |