Identification methods for Wiener nonlinear systems based on the least squares and gradient iterations

This paper derives a least squares based and a gradient based iterative identification algorithms for Wiener nonlinear systems. These methods separate one bilinear-parameter cost function into two linear-parameter cost functions, estimating directly the parameters of the Wiener systems. The simulati...

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Bibliographic Details
Published inProceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference pp. 3632 - 3636
Main Authors Dongqing Wang, Yanyun Chu, Feng Ding
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2009
Subjects
Convergence
Cost function
Educational institutions
Hammerstein models
Iterative algorithms
iterative identification
Iterative methods
least squares
Least squares approximation
Least squares methods
Nonlinear dynamical systems
Nonlinear systems
Parameter estimation
recursive identification
stochastic gradient
System modelling
Wiener models
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Summary:This paper derives a least squares based and a gradient based iterative identification algorithms for Wiener nonlinear systems. These methods separate one bilinear-parameter cost function into two linear-parameter cost functions, estimating directly the parameters of the Wiener systems. The simulation results confirm that the proposed two algorithms are valid and the least squares based iterative algorithm has faster convergence rates than the gradient based iterative algorithm.
ISBN:9781424438716
1424438713
ISSN:0191-2216
DOI:10.1109/CDC.2009.5399834