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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Veröffentlicht in:Journal of the Franklin Institute Jg. 357; H. 8; S. 5019 - 5032
Hauptverfasser: Ji, Yan, Jiang, Xiaokun, Wan, Lijuan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elmsford Elsevier Ltd 01.05.2020
Elsevier Science Ltd
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ISSN:0016-0032, 1879-2693, 0016-0032
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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content type line 14
ISSN:0016-0032
1879-2693
0016-0032
DOI:10.1016/j.jfranklin.2020.03.027