稀疏系统建模的低复杂度集员规范化LMS算法
Low-Complexity Set-Membership Normalized LMS Algorithm for Sparse System Modeling
摘要：
在这项工作中，我们提出了两个低复杂度的集员归一化最小均方（LCSM-NLMS 1和LCSM-NLMS 2）算法，利用未知系统的稀疏性。为此，在LCSM-NLMS 1算法中，我们采用了一种称为丢弃函数的自适应系数，以忽略在更新过程中接近零的系数。此外，在LCSM-NLMS 2算法中，为了进一步减少所需的总计算量，我们将小系数替换为零。数值结果表明，这些算法与一些最先进的稀疏感知算法相比，具有相似的性能，而所提出的算法需要更低的计算成本。
英文摘要：
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.
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