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A Recursive Identification Algorithm for Wiener Nonlinear Systems with Linear State-Space Subsystem

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Abstract

This paper addresses the problem of recursive identification of Wiener nonlinear systems whose linear subsystems are observable state-space models. The maximum likelihood principle and the recursive identification technique are employed to develop a recursive maximum likelihood identification algorithm which estimates the unknown parameters and the system states interactively. In comparison with the developed recursive maximum likelihood algorithm, a recursive generalized least squares algorithm is also proposed for identification of such Wiener systems. The performance of the developed algorithms is validated by two illustrative examples.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (61403217, 61273024), the Jiangsu Province Postdoctoral Research Funding Plan (1601129B), the Jiangsu Government Scholarship for Overseas Studies, and the Australian Research Council (DP120104986).

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Authors and Affiliations

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

    Junhong Li, Juping Gu & Liang Hua

  2. School of Computing, Engineering and Mathematics, Western Sydney University, Sydney, NSW, 2751, Australia

    Junhong Li & Wei Xing Zheng

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  1. Junhong Li
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  2. Wei Xing Zheng
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  3. Juping Gu
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  4. Liang Hua
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Correspondence to Wei Xing Zheng.

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Li, J., Zheng, W.X., Gu, J. et al. A Recursive Identification Algorithm for Wiener Nonlinear Systems with Linear State-Space Subsystem. Circuits Syst Signal Process 37, 2374–2393 (2018). https://doi.org/10.1007/s00034-017-0682-7

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  • DOI: https://doi.org/10.1007/s00034-017-0682-7

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