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Recursive Least Squares Parameter Estimation for a Class of Output Nonlinear Systems Based on the Model Decomposition

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Abstract

In this paper, we study the parameter estimation problem of a class of output nonlinear systems and propose a recursive least squares (RLS) algorithm for estimating the parameters of the nonlinear systems based on the model decomposition. The proposed algorithm has lower computational cost than the existing over-parameterization model-based RLS algorithm. The simulation results indicate that the proposed algorithm can effectively estimate the parameters of the nonlinear systems.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (No. 61203111) 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

    Feng Ding, Xuehai Wang, Qijia Chen & Yongsong Xiao

Authors
  1. Feng Ding
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  2. Xuehai Wang
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  3. Qijia Chen
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  4. Yongsong Xiao
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Correspondence to Feng Ding.

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Ding, F., Wang, X., Chen, Q. et al. Recursive Least Squares Parameter Estimation for a Class of Output Nonlinear Systems Based on the Model Decomposition. Circuits Syst Signal Process 35, 3323–3338 (2016). https://doi.org/10.1007/s00034-015-0190-6

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  • DOI: https://doi.org/10.1007/s00034-015-0190-6

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