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Maximum likelihood Newton recursive and the Newton iterative estimation algorithms for Hammerstein CARAR systems

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Abstract

This paper discusses the identification problems of Hammerstein controlled autoregressive autoregressive (CARAR) systems using the maximum likelihood principle and Newton optimization method. A Newton recursive algorithm and a Newton iterative algorithm using the maximum likelihood principle are presented. The simulation results show that the proposed algorithms can effectively estimate the parameters of the Hammerstein CARAR systems.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (61273194, 61305031, 51307089), the Natural Science Foundation for Colleges and Universities of Jiangsu Province (12KJA51002), the Nantong Science and Technology Project (BK2012060), the PAPD of Jiangsu Higher Education Institutions.

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Authors and Affiliations

  1. School of Electrical Engineering, Nantong University, Nantong, 226019, P.R. China

    Junhong Li & Liang Hua

  2. School of Internet of Things Engineering, Jiangnan University, Wuxi, 214122, P.R. China

    Junhong Li & Feng Ding

Authors
  1. Junhong Li
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  2. Feng Ding
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  3. Liang Hua
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Correspondence to Feng Ding.

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Li, J., Ding, F. & Hua, L. Maximum likelihood Newton recursive and the Newton iterative estimation algorithms for Hammerstein CARAR systems. Nonlinear Dyn 75, 235–245 (2014). https://doi.org/10.1007/s11071-013-1061-y

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  • DOI: https://doi.org/10.1007/s11071-013-1061-y

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