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Parameter Estimation of Wiener Systems Based on the Particle Swarm Iteration and Gradient Search Principle

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Abstract

The Wiener nonlinear system is composed of a linear dynamic subsystem in series with a static nonlinear subsystem. This type of system is widely found in the petroleum, chemistry, thermal and other process industries. It is of great significance to obtain the parameter estimates of the Wiener systems. This paper studies the identification problem of the Wiener time delay nonlinear system. Based on the gradient search principle, a stochastic gradient identification algorithm and a gradient-based iterative identification algorithm are derived. Furthermore, a linearly decreasing weight particle swarm iterative identification algorithm is also proposed for the discussed Wiener time delay systems. Finally, a numerical example and two application cases are given for validating the feasibility of the three identification methods. The results demonstrate that the three algorithms can identify the unknown parameters of the Wiener model effectively. Moreover, the linearly decreasing weight particle swarm iterative identification algorithm behaves much better than the stochastic gradient and the gradient-based iterative algorithms in accuracy and convergence speed.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (61973176, 61973178), the Jiangsu Natural Science Foundation (BK20181457), the Natural science foundation for colleges and universities in Jiangsu Province (18KJB120007) and the Six Talent Peak Projects in Jiangsu Province (XYDXX-038).

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Authors and Affiliations

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

    Junhong Li, Tiancheng Zong, Juping Gu & Liang Hua

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  1. Junhong Li
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  2. Tiancheng Zong
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  4. Liang Hua
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Correspondence to Junhong Li.

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Li, J., Zong, T., Gu, J. et al. Parameter Estimation of Wiener Systems Based on the Particle Swarm Iteration and Gradient Search Principle. Circuits Syst Signal Process 39, 3470–3495 (2020). https://doi.org/10.1007/s00034-019-01329-1

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