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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Vydáno v:Journal of the Franklin Institute Ročník 357; číslo 8; s. 5019 - 5032
Hlavní autoři: Ji, Yan, Jiang, Xiaokun, Wan, Lijuan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elmsford Elsevier Ltd 01.05.2020
Elsevier Science Ltd
Témata:
Algorithms
Estimating techniques
Hierarchical identification principle
Impulse response
Innovations
Least squares
Mathematical models
Parameter estimation
Parameter identification
Parameterization
Recursive identification
System effectiveness
Recursive identification
Parameter estimation
Hierarchical identification principle
Least squares
ISSN:0016-0032, 1879-2693, 0016-0032
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Shrnutí: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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ISSN:0016-0032
1879-2693
0016-0032
DOI:10.1016/j.jfranklin.2020.03.027