论文标题
与Tukey的Biweight M-结合梯度自适应学习
Conjugate Gradient Adaptive Learning with Tukey's Biweight M-Estimate
论文作者
论文摘要
我们提出了一种新型的M估计结合梯度（CG）算法，称为Tukey的Biweight M-估计CG（TBMCG），用于在冲动噪声环境中进行系统识别。特别地，与递归最小二乘（RLS）算法相比，TBMCG算法可以达到更快的收敛速度，同时保留了降低的计算复杂性。具体而言，Tukey的Biweight M-估计数将约束结合到CG过滤器中，以应对冲动的噪声环境。此外，分析了TBMCG算法的收敛行为。仿真结果证实了针对系统识别和主动噪声控制应用的提出的TBMCG算法的出色性能。
We propose a novel M-estimate conjugate gradient (CG) algorithm, termed Tukey's biweight M-estimate CG (TbMCG), for system identification in impulsive noise environments. In particular, the TbMCG algorithm can achieve a faster convergence while retaining a reduced computational complexity as compared to the recursive least-squares (RLS) algorithm. Specifically, the Tukey's biweight M-estimate incorporates a constraint into the CG filter to tackle impulsive noise environments. Moreover, the convergence behavior of the TbMCG algorithm is analyzed. Simulation results confirm the excellent performance of the proposed TbMCG algorithm for system identification and active noise control applications.
