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Nonlinear spline prioritization optimization adaptive filter with arctangent-exponential hyperbolic cosine

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Abstract

This paper presents a new spline prioritization optimization adaptive filter with arctangent-exponential hyperbolic cosine (SPOAF-ARC-EHC) to solve the high steady-state error in colored and/or impulsive noise environments. Different from the traditional spline adaptive filtering algorithm, the weight update of the spline prioritization optimization adaptive filter (SPOAF) is only based on the linear error, and the update of the spline control points is based on the error of the whole system. To improve the robustness of the proposed SPOAF algorithm, the linear part and the nonlinear part use independent cost functions. More specifically, we utilize the arctangent function (ARC) as the cost function for the linear part and the exponential hyperbolic cosine as the loss function for the nonlinear part. Through simulation, it is shown that the proposed algorithm significantly reduces the steady-state error compared with the existing algorithms in a colored and/or impulsive noise environment.

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

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Funding

This research was supported by the National Natural Science Foundation of China (Grant Nos. U20B2040 and 61671379).

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Authors and Affiliations

  1. School of Automation, Northwestern Polytechnical University, Xi’an Shaanxi, P.R. China

    Wenyan Guo & Yongfeng Zhi

  2. Science and Technology on Electro-Optical Information Security Control Laboratory, Tianjin, China

    Kai Feng

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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. & Feng, K. Nonlinear spline prioritization optimization adaptive filter with arctangent-exponential hyperbolic cosine. Nonlinear Dyn 110, 611–621 (2022). https://doi.org/10.1007/s11071-022-07636-8

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  • DOI: https://doi.org/10.1007/s11071-022-07636-8

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