Correntropy Induced Metric Penalized Sparse RLS Algorithm to Improve Adaptive System Identification
Correntropy Induced Metric Penalized Sparse RLS Algorithm to Improve Adaptive System Identification
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DOI：
10.1109/vtcspring.2016.7504179
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发表时间：
2016-07
期刊：
影响因子：
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通讯作者：
Guan Gui;L. Dai;B. Zheng;Li Xu;F. Adachi
中科院分区：
文献类型：
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作者：
Guan Gui;L. Dai;B. Zheng;Li Xu;F. Adachi
稀疏自适应滤波算法被用来利用潜在的稀疏结构信息，以及在许多未知的稀疏系统中减轻噪声。稀疏递归最小二乘（RLS）算法因其复杂度低、易于实现而受到广泛关注。基本上，这些算法由标准RLS算法和稀疏罚函数（例如，l_1-norm）。然而，现有的稀疏RLS算法没有有效地利用稀疏性。本文将一种新的相关熵诱导度量（CIM）约束引入到RLS中，提出了一种改进的自适应滤波算法，称为RLS-CIM算法。具体来说，我们采用了一个著名的高斯内核在CIM，并进一步设计了一个新的可变内核宽度来控制稀疏惩罚在不同的瞬态错误的情况下。数值仿真结果证实了所提出的算法通过均方差（MSD）。
Sparse adaptive filtering algorithms are utilized to exploit potential sparse structure information as well as to mitigate noises in many unknown sparse systems. Sparse recursive least square (RLS) algorithms have been attracted intensely attentions due to their low-complexity and easy- implementation. Basically, these algorithms are constructed by standard RLS algorithm and sparse penalty functions (e.g., l_1-norm). However, existing sparse RLS algorithms do not exploit the sparsity efficiently. In this paper, an improved adaptive filtering algorithm is proposed by incorporating a novel correntropy induced metric (CIM) constraint into RLS, which is termed as RLS- CIM algorithm. Specifically, we adopt a well-known Gaussian kernel in CIM and further devise a novel variable kernel width to control the sparse penalty in different transient-error scenarios. Numerical simulation results are given to corroborate the proposed algorithm via mean square deviation (MSD).