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Maximum Likelihood Recursive Least Squares Estimation for Multivariable Systems

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Abstract

This paper discusses parameter estimation problems of the multivariable systems described by input–output difference equations. We decompose a multivariable system to several subsystems according to the number of the outputs. Based on the maximum likelihood principle, a maximum likelihood-based recursive least squares algorithm is derived to estimate the parameters of each subsystem. Finally, two numerical examples are provided to verify the effectiveness of the proposed algorithm.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (61273024, 61305031, 51307089), the Nantong Science and Technology Project (BK2012060), the Industrialization Project for Colleges and Universities of Jiangsu Province (JHB2012-44).

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Authors and Affiliations

  1. School of Electrical Engineering, Nantong University, Nantong, 226019, People’s Republic of China

    Junhong Li & Ping Jiang

  2. School of Internet of Things Engineering, Jiangnan University, Wuxi, 214122, People’s Republic of China

    Feng Ding

  3. Laboratory of Underwater Vehicles and Intelligent Systems, Shanghai Maritime University, Shanghai, 201306, People’s Republic of China

    Daqi Zhu

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  1. Junhong Li
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  2. Feng Ding
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  3. Ping Jiang
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  4. Daqi Zhu
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Correspondence to Junhong Li.

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Li, J., Ding, F., Jiang, P. et al. Maximum Likelihood Recursive Least Squares Estimation for Multivariable Systems. Circuits Syst Signal Process 33, 2971–2986 (2014). https://doi.org/10.1007/s00034-014-9783-8

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  • DOI: https://doi.org/10.1007/s00034-014-9783-8

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