论文标题
稀疏系统建模的低复杂设置成员标准化的LMS算法
Low-Complexity Set-Membership Normalized LMS Algorithm for Sparse System Modeling
论文作者
论文摘要
在这项工作中，我们提出了两种低复杂性设置会员归一化最小均值平方（LCSM-NLMS1和LCSM-NLMS2）算法，以利用未知系统的稀疏性。为此，在LCSM-NLMS1算法中，我们采用了一个名为“丢弃功能”的函数，以忽略更新过程中接近零的系数。此外，在LCSM-NLMS2算法中，为了减少所需的总体数量，我们替代了零的小系数。当这些算法与某些最新的稀疏感知算法进行比较时，数值结果的性能相似，而所提出的算法则需要较低的计算成本。
In this work, we propose two low-complexity set-membership normalized least-mean-square (LCSM-NLMS1 and LCSM-NLMS2) algorithms to exploit the sparsity of an unknown system. For this purpose, in the LCSM-NLMS1 algorithm, we employ a function called the discard function to the adaptive coefficients in order to neglect the coefficients close to zero in the update process. Moreover, in the LCSM-NLMS2 algorithm, to decrease the overall number of computations needed even further, we substitute small coefficients with zero. Numerical results present similar performance of these algorithms when comparing them with some state-of-the-art sparsity-aware algorithms, whereas the proposed algorithms need lower computational cost.
