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Convex regularized recursive maximum correntropy algorithm

机译:凸正则化递归最大熵

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摘要

In this brief, a robust and sparse recursive adaptive filtering algorithm, called convex regularized recursive maximum correntropy (CR-RMC), is derived by adding a general convex regularization penalty term to the maximum correntropy criterion (MCC). An approximate expression for automatically selecting the regularization parameter is also introduced. Simulation results show that the CR-RMC can significantly outperform the original recursive maximum correntropy (RMC) algorithm especially when the underlying system is very sparse. Compared with the convex regularized recursive least squares (CR-RLS) algorithm, the new algorithm also shows strong robustness against impulsive noise. The CR-RMC also performs much better than other LMS-type sparse adaptive filtering algorithms based on MCC.
机译:在本文中,通过将一个一般的凸正则化惩罚项添加到最大熵准则(MCC)中,得出了一种鲁棒且稀疏的递归自适应滤波算法,称为凸正则化递归最大熵(CR-RMC)。还介绍了用于自动选择正则化参数的近似表达式。仿真结果表明,CR-RMC可以显着优于原始的递归最大熵(RMC)算法,尤其是在底层系统非常稀疏的情况下。与凸正则化递归最小二乘算法(CR-RLS)相比,该新算法还具有较强的抗脉冲噪声鲁棒性。 CR-RMC的性能也比其他基于MCC的LMS型稀疏自适应滤波算法要好得多。

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