Transient Performance Analysis of Zero-Attracting Gaussian Kernel LMS Algorithm With Pre-Tuned Dictionary

IEEE Access

Jie Chen¹  Wei Gao² 

[1] Center of Intelligent Acoustics and Immersive Communications (CIAIC), School of Marine Science and Technology, Northwestern Polytechnical University, Xi&x2019;School of Computer Science and Telecommunication Engineering, Jiangsu University, Zhenjiang, China;

关键词: Nonlinear sparse system identification;    zero-attracting;    kernel least-mean-square;    transient performance analysis;   

DOI  :  10.1109/ACCESS.2019.2942088

来源: DOAJ

【 摘 要 】

Although the sparse kernel adaptive filtering algorithms have been proposed to address the problem of redundant dictionary in non-stationary environments, there is few attempt of analyzing their stochastic convergence behaviors. In this paper, we briefly review the zero-attracting kernel leastmean-square (ZA-KLMS) algorithm with ℓ₁-norm regularization from the perspective of nonlinear sparse system. Then, the theoretical transient convergence performance of ZA-KLMS algorithm using Gaussian kernel function with pre-tuned dictionary is analyzed in the mean and mean-square senses. The simulation results illustrate the accuracy of derived analytical models by the excellent consistency between the Monte Carlo simulations and the theoretical predictions, and the ZA-KLMS algorithm has better convergence performance than the KLMS algorithm for nonlinear sparse systems in stationary environment.

【 授权许可】

Unknown