New algorithms for improved adaptive convex combination of LMS transversal filters
New algorithms for improved adaptive convex combination of LMS transversal filters
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DOI：
10.1109/tim.2005.858823
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发表时间：
2005-11
影响因子：
5.6
通讯作者：
J. Arenas-García;V. Gómez-Verdejo;A. Figueiras-Vidal
中科院分区：
文献类型：
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作者：
J. Arenas-García;V. Gómez-Verdejo;A. Figueiras-Vidal
在所有自适应滤波算法中，Widrow 和 Hoff 的最小均方 (LMS) 可能因其鲁棒性、良好的跟踪特性和简单性而成为最受欢迎的算法。 LMS 的缺点是步长意味着收敛速度和最终失调之间的折衷。组合不同速度的 LMS 滤波器可以缓解这种妥协，正如我们对两个滤波器组合（我们称之为 LMS 滤波器组合 (CLMS)）的研究所证明的那样。在这里，我们从两个方向扩展这个方案。首先，我们提出了一种概括，将多个 LMS 滤波器与不同的步骤组合起来，为组合提供更好的跟踪能力。其次，我们对滤波器的每个权重使用不同的混合参数，以使它们的适应速度独立。与 CLMS 滤波器和其他先前的可变步长方法相比，植物识别和噪声消除应用中的一些仿真示例表明了新方案的有效性。
Among all adaptive filtering algorithms, Widrow and Hoff's least mean square (LMS) has probably become the most popular because of its robustness, good tracking properties and simplicity. A drawback of LMS is that the step size implies a compromise between speed of convergence and final misadjustment. To combine different speed LMS filters serves to alleviate this compromise, as it was demonstrated by our studies on a two filter combination that we call combination of LMS filters (CLMS). Here, we extend this scheme in two directions. First, we propose a generalization to combine multiple LMS filters with different steps that provides the combination with better tracking capabilities. Second, we use a different mixing parameter for each weight of the filter in order to make independent their adaption speeds. Some simulation examples in plant identification and noise cancellation applications show the validity of the new schemes when compared to the CLMS filter and to other previous variable step approaches.