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Gradient-Based Recursive Identification Methods for Input Nonlinear Equation Error Closed-Loop Systems

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Abstract

The identification problem of closed-loop or feedback nonlinear systems is a hot topic. Based on the hierarchical identification principle, this paper presents a hierarchical stochastic gradient algorithm and a hierarchical multi-innovation stochastic gradient algorithm for feedback nonlinear systems. The simulation results show that the hierarchical multi-innovation stochastic gradient can more effectively estimate the parameters of the feedback nonlinear systems than the hierarchical stochastic gradient algorithm.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (No. 61273194) and the 111 Project (B12018).

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Authors and Affiliations

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

    Bingbing Shen & Feng Ding

  2. Department of Mathematics, Nonlinear Analysis and Applied Mathematics (NAAM) Research Group, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

    Feng Ding, Ahmed Alsaedi & Tasawar Hayat

  3. Department of Mathematics, Quaid-I-Azam University, Islamabad, 44000, Pakistan

    Tasawar Hayat

Authors
  1. Bingbing Shen
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  2. Feng Ding
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  3. Ahmed Alsaedi
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  4. Tasawar Hayat
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Correspondence to Feng Ding.

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Shen, B., Ding, F., Alsaedi, A. et al. Gradient-Based Recursive Identification Methods for Input Nonlinear Equation Error Closed-Loop Systems. Circuits Syst Signal Process 36, 2166–2183 (2017). https://doi.org/10.1007/s00034-016-0394-4

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  • DOI: https://doi.org/10.1007/s00034-016-0394-4

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