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

被引：2
|
作者：
Xu, Yonghui [1 ]
Yang, Zixuan [1 ]
Liu, Yuqi [1 ,2 ]
Jiang, Shouda [1 ]
机构：
[1] Harbin Inst Technol, Dept Elect Engn & Automat, Harbin 150001, Peoples R China
[2] China Inst Marine Technol & Econ, Beijing 100089, Peoples R China
来源：
IEEE ACCESS | 2020年 / 8卷 / 08期
关键词：
Signal processing algorithms; Kernel; Convergence; Approximation algorithms; Heuristic algorithms; Prediction algorithms; Acceleration; Random Fourier features; forgetting factor strategy; variable step size; Kernel least-mean-square algorithm; RLS ALGORITHM; LEAST; NETWORKS;
D O I：
10.1109/ACCESS.2020.2975536
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：126887 / 126895
页数：9
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