Hierarchical least squares parameter estimation algorithm for two-input Hammerstein finite impulse response systems
This paper considers the parameter estimation problems of two-input single-output Hammerstein finite impulse response systems. A hierarchical least squares algorithm is proposed for improving the computational efficiency through combining the hierarchical identification principle and the identificat...
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| Published in | Journal of the Franklin Institute Vol. 357; no. 8; pp. 5019 - 5032 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Elmsford
Elsevier Ltd
01.05.2020
Elsevier Science Ltd |
| Subjects | |
| Online Access | Get full text |
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| Abstract | This paper considers the parameter estimation problems of two-input single-output Hammerstein finite impulse response systems. A hierarchical least squares algorithm is proposed for improving the computational efficiency through combining the hierarchical identification principle and the identification model decomposition, and a multi-innovation least squares algorithm is proposed for enhancing the parameter estimation accuracy based on the multi-innovation identification theory. The key is to derive two sub-identification models, each of which contains a set of merged parameter vectors. The proposed algorithm is effective and can generate highly accurate parameter estimates compared with the over-parametrization identification method, and can be easily extended to multi-input multi-output systems. Finally, an illustrative example is provided to verify the effectiveness of the proposed algorithm. |
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| AbstractList | This paper considers the parameter estimation problems of two-input single-output Hammerstein finite impulse response systems. A hierarchical least squares algorithm is proposed for improving the computational efficiency through combining the hierarchical identification principle and the identification model decomposition, and a multi-innovation least squares algorithm is proposed for enhancing the parameter estimation accuracy based on the multi-innovation identification theory. The key is to derive two sub-identification models, each of which contains a set of merged parameter vectors. The proposed algorithm is effective and can generate highly accurate parameter estimates compared with the over-parametrization identification method, and can be easily extended to multi-input multi-output systems. Finally, an illustrative example is provided to verify the effectiveness of the proposed algorithm. |
| Author | Wan, Lijuan Jiang, Xiaokun Ji, Yan |
| Author_xml | – sequence: 1 givenname: Yan surname: Ji fullname: Ji, Yan email: yjichina@163.com – sequence: 2 givenname: Xiaokun surname: Jiang fullname: Jiang, Xiaokun – sequence: 3 givenname: Lijuan surname: Wan fullname: Wan, Lijuan |
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| SubjectTerms | Algorithms Estimating techniques Hierarchical identification principle Impulse response Innovations Least squares Mathematical models Parameter estimation Parameter identification Parameterization Recursive identification System effectiveness |
| Title | Hierarchical least squares parameter estimation algorithm for two-input Hammerstein finite impulse response systems |
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