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Recursive least squares algorithm and gradient algorithm for Hammerstein–Wiener systems using the data filtering

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Abstract

This paper considers the parameter estimation problems of Hammerstein–Wiener systems by using the data filtering technique. In order to improve the estimation accuracy, the data filtering-based recursive generalized extended least squares algorithm is derived. In order to improve the computational efficiency, the data filtering-based generalized extended stochastic gradient algorithm is derived for estimating the system parameters. Finally, the computational efficiency of the proposed algorithms is analyzed and compared. The simulation results indicate that the proposed algorithms can effectively estimate the parameters of Hammerstein–Wiener systems.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (No. 61273194) and the PAPD of Jiangsu Higher Education Institutions.

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Authors and Affiliations

  1. Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi, 214122, People’s Republic of China

    Yanjiao Wang & Feng Ding

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  1. Yanjiao Wang
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  2. Feng Ding
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Correspondence to Feng Ding.

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Wang, Y., Ding, F. Recursive least squares algorithm and gradient algorithm for Hammerstein–Wiener systems using the data filtering. Nonlinear Dyn 84, 1045–1053 (2016). https://doi.org/10.1007/s11071-015-2548-5

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  • DOI: https://doi.org/10.1007/s11071-015-2548-5

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