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Nonlinear spline prioritization optimization generalized hyperbolic secant adaptive filtering against alpha-stable noise

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Abstract

In order to mitigate the interference of impulsive noises in the identification of nonlinear systems using spline prioritization optimization adaptive filtering (SPOAF) algorithm, an enhanced SPOAF based on generalized hyperbolic secant (GHS) function, named SPOAF–GHS, is proposed in this paper. The convergence property of the proposed algorithm has been theoretically analyzed. Two improvements have been made in the proposed algorithm. On the one hand, to improve the convergence speed in the adaptive process, the momentum Nesterov accelerated gradient optimization algorithm has been introduced. In addition, a frequency domain filtering architecture model has been proposed to address the high computational complexity of the filtering algorithm caused by the presence of bigger number of taps during the filtering process. Several numerical experiments are conducted, and the results show that the proposed GHS-type algorithms have superior performance compared to existing spline interpolation filtering algorithms.

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Data Availability

The datasets generated or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant U20B2040, Grant 61671379.

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  1. School of Automation, Northwestern Polytechnical University, Shaanxi, 710129, China

    Wenyan Guo & Yongfeng Zhi

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  1. Wenyan Guo
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  2. Yongfeng Zhi
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Correspondence to Yongfeng Zhi.

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Guo, W., Zhi, Y. Nonlinear spline prioritization optimization generalized hyperbolic secant adaptive filtering against alpha-stable noise. Nonlinear Dyn 111, 14351–14363 (2023). https://doi.org/10.1007/s11071-023-08583-8

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