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

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

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...

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Bibliographic Details
Main Authors: Yonghui Xu, Zixuan Yang, Yuqi Liu, Shouda Jiang
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Random Fourier features
forgetting factor strategy
variable step size
Kernel least-mean-square algorithm
Online Access:https://ieeexplore.ieee.org/document/9006877/

Internet

https://ieeexplore.ieee.org/document/9006877/

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