DOAJ Open Access 2020

Accelerate Convergence of Polarized Random Fourier Feature-Based Kernel Adaptive Filtering With Variable Forgetting Factor and Step Size

Yonghui Xu Zixuan Yang Yuqi Liu Shouda Jiang

Abstrak

The random Fourier feature as an efficient kernel approximation method can effectively suppress the network growth of the traditional kernel-based adaptive filtering algorithm. Polarized random Fourier feature kernel least-mean-square(PRFFKLMS) remarkably improved the accuracy performance of random Fourier feature-based kernel least-mean-square algorithm and become the most representative random Fourier feature-based least-mean-square algorithm. In this paper, we studied the method that can improve the convergence speed of random Fourier feature-based least-mean-square algorithm. Based on the variable forgetting factor and variable step size strategy, three algorithm are proposed. The computational complexity of proposed algorithms are also given. The simulation results show that compared with PRFFKLMS algorithm, the convergence speed of the proposed algorithm is significantly improved.

Topik & Kata Kunci

Electrical engineering. Electronics. Nuclear engineering

Penulis (4)

Yonghui Xu

Zixuan Yang

Yuqi Liu

Shouda Jiang

Format Sitasi

APA MLA BibTeX

Xu, Y., Yang, Z., Liu, Y., Jiang, S. (2020). Accelerate Convergence of Polarized Random Fourier Feature-Based Kernel Adaptive Filtering With Variable Forgetting Factor and Step Size. https://doi.org/10.1109/ACCESS.2020.2975536

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Informasi Jurnal

Tahun Terbit: 2020
Sumber Database: DOAJ
DOI: 10.1109/ACCESS.2020.2975536
Akses: Open Access ✓