Iterative Learning Identification for a Class of Wiener Nonlinear Time- Varying Systems

Guomin ZHONG; Mingxuan SUN

doi:10.11999/JEIT200882

Volume 43 Issue 9

Sep. 2021

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Guomin ZHONG, Mingxuan SUN. Iterative Learning Identification for a Class of Wiener Nonlinear Time- Varying Systems[J]. Journal of Electronics & Information Technology, 2021, 43(9): 2594-2600. doi: 10.11999/JEIT200882

Citation:

Guomin ZHONG, Mingxuan SUN. Iterative Learning Identification for a Class of Wiener Nonlinear Time- Varying Systems[J]. Journal of Electronics & Information Technology, 2021, 43(9): 2594-2600. doi: 10.11999/JEIT200882

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Iterative Learning Identification for a Class of Wiener Nonlinear Time- Varying Systems

doi: 10.11999/JEIT200882

Guomin ZHONG^,,
Mingxuan SUN

College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China

Funds: The National Natural Science Foundation of China(62073291)

Received Date: 2020-10-16
Rev Recd Date: 2021-03-11

Available Online: 2021-04-15

Publish Date: 2021-09-16

Abstract

Abstract

For the parameters identification of Wiener nonlinear time-varying systems, iterative learning algorithms based on repeated axes are proposed to estimate the time-varying or even abrupt parameters. At first, the output nonlinear part of the Wiener system undertaken is tackled based on polynomial expansion, and then the regression model is constructed, the unknown parameters and intermediate variables are replaced by their estimates. Both iterative learning gradient and iterative learning least square algorithms are used to conduct the identification of the time-varying systems. Compared with the recursive algorithm with forgetting factor and iterative learning gradient algorithm, the simulation results demonstrate that the iterative learning least squares algorithm can perform high identification accuracy and efficiency, being of fast convergence speed and less resultant system output error, which verifies the effectiveness of the proposed algorithm.
- Time-varying parameters,
- Wiener systems,
- Gradient algorithms,
- Least squares,
- Iterative learning

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References(21)

References

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