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A new Gaussian Kernel Filtering Algorithm Involving the Sparse Criterion

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Abstract

Kernel adaptive filtering algorithms have been successfully applied in many areas, among which the fraction lower power order statistics error criterion (FLP) is a better choice due to its prominent performance in robust design. However, the growth of network size in kernel adaptive filtering affects the convergence rate and testing accuracy. This paper proposes a sparse Gaussian kernel adaptive filtering algorithm based on the Softplus function framework to address the network size. The framework is constituted by the fraction lower power order statistics error criterion and an improved novelty criterion. The exponential loss function is employed to describe a new novel criterion, which can accurately achieve fast classification and limit the data selection of the dictionary size. This new algorithm is called the kernel fraction lower power order statistics error criterion based on the Softplus function with a modified novelty criterion (SKFLP-MNC) algorithm. In particular, the proposed method is employed for the Mackey–Glass chaotic time series prediction and noise cancellation under the cases of Gaussian and non-Gaussian noise. Simulation results show that the filtering accuracy of the SKFLP-MNC algorithm, the dictionary size, and steady-state mean-squared errors rival some of the sparse kernel least mean square algorithms.

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Funding

Funding was provided by national natural science foundation of china (Grant no. 61561044).

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Authors and Affiliations

  1. College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou, 730000, China

    Yuanlian Huo, Danfeng Wang & Peijun Lian

  2. College of Computer Science and Engineering, Northwest Normal University, Lanzhou, 730000, China

    Yongfeng Qi

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  1. Yuanlian Huo
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  2. Danfeng Wang
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Correspondence to Danfeng Wang.

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Huo, Y., Wang, D., Qi, Y. et al. A new Gaussian Kernel Filtering Algorithm Involving the Sparse Criterion. Circuits Syst Signal Process 42, 522–539 (2023). https://doi.org/10.1007/s00034-022-02139-8

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  • DOI: https://doi.org/10.1007/s00034-022-02139-8

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