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首页> 外文期刊>Signal Processing, IET >Robust zero-point attraction least mean square algorithm on near sparse system identification
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Robust zero-point attraction least mean square algorithm on near sparse system identification

机译：近似稀疏系统辨识的鲁棒零点吸引最小均方算法

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摘要

The newly proposed l1 norm constraint zero-point attraction least mean square algorithm (ZA-LMS) demonstrates excellent performance on exact sparse system identification. However, ZA-LMS has less advantage against standard LMS when the system is near sparse. Thus, in this study, firstly the near sparse system (NSS) modelling by generalised Gaussian distribution is recommended, where the sparsity is defined accordingly. Second, two modifications to the ZA-LMS algorithm have been made. The l1 norm penalty is replaced by a partial l1 norm in the cost function, enhancing robustness without increasing the computational complexity. Moreover, the ZA item is weighted by the magnitude of estimation error which adjusts the ZA force dynamically. By combining the two improvements, Dynamic Windowing ZA-LMS (DWZA-LMS) algorithm is further proposed, which shows better performance on NSS identification. In addition, the mean-square performance of DWZA-LMS algorithm is analysed. Finally, computer simulations demonstrate the effectiveness of the proposed algorithm and verify the result of theoretical analysis.
机译：新提出的l1范数约束零点吸引最小均方算法（ZA-LMS）在精确的稀疏系统识别方面表现出出色的性能。但是，当系统接近稀疏时，ZA-LMS相对于标准LMS的优势较小。因此，在这项研究中，首先建议使用广义高斯分布的近稀疏系统（NSS）建模，其中稀疏度是相应定义的。其次，对ZA-LMS算法进行了两次修改。用代价函数中的部分l1范数代替l1范数惩罚，从而在不增加计算复杂度的情况下增强了鲁棒性。此外，ZA项目由估计误差的大小加权，该误差会动态调整ZA力。通过结合这两种改进，进一步提出了动态窗口化ZA-LMS（DWZA-LMS）算法，该算法在NSS识别方面表现出更好的性能。另外，分析了DWZA-LMS算法的均方性能。最后，计算机仿真证明了该算法的有效性，并验证了理论分析的结果。

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