论文标题
数据选择性Volterra NLMS算法的鲁棒性分析
Robustness Analysis of the Data-Selective Volterra NLMS Algorithm
论文作者
论文摘要
最近，已经提出了数据选择性自适应伏特拉过滤器。但是，到目前为止，还没有关于其行为而不是数值模拟的理论分析。因此，在本文中，我们分析了数据选择性Volterra的鲁棒性（从L2稳定性的意义上），将最小值均值（DS-VNLMS）算法归一化。首先，我们在任何迭代中研究了该算法的局部鲁棒性，然后我们提出了系数载体中误差/差异的全局界限。另外，我们证明DS-VNLMS算法改善了实现更新的大多数迭代的参数估计。此外，我们证明，如果已知噪声结合，我们可以设置DS-VNLMS，以免它降低估计值。模拟结果证实了执行分析的有效性，并证明了DS-VNLMS算法对噪声的稳健性，无论采用其参数如何。
Recently, the data-selective adaptive Volterra filters have been proposed; however, up to now, there are not any theoretical analyses on its behavior rather than numerical simulations. Therefore, in this paper, we analyze the robustness (in the sense of l2-stability) of the data-selective Volterra normalized least-mean-square (DS-VNLMS) algorithm. First, we study the local robustness of this algorithm at any iteration, then we propose a global bound for the error/discrepancy in the coefficient vector. Also, we demonstrate that the DS-VNLMS algorithm improves the parameter estimation for the majority of the iterations that an update is implemented. Moreover, we prove that if the noise bound is known, we can set the DS-VNLMS so that it never degrades the estimate. The simulation results corroborate the validity of the executed analysis and demonstrate that the DS-VNLMS algorithm is robust against noise, no matter how its parameters are adopted.
