A variable leaky LMS adaptive algorithm

A variable leaky LMS adaptive algorithm
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一种可变泄漏LMS自适应算法

DOI：
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发表时间：
2004
期刊：
Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, 2004.
影响因子：
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通讯作者：
Bernard Widrow
Bernard Widrow
中科院分区：
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文献类型：
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作者：
Max Kamenetsky;Bernard Widrow

文献摘要

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LMS算法在自适应信号处理和控制的许多领域得到了广泛的应用。我们介绍了一种可变泄漏LMS算法，旨在克服标准LMS的高输入特征值扩展的情况下，收敛速度慢。该算法使用了贪婪的惩罚/奖励启发式与量化的泄漏调整功能，以改变泄漏。仿真结果表明，当输入特征值扩展较大时，新算法的性能明显优于标准LMS算法。
The LMS algorithm has found wide application in many areas of adaptive signal processing and control. We introduce a variable leaky LMS algorithm, designed to overcome the slow convergence of standard LMS in cases of high input eigenvalue spread. The algorithm uses a greedy punish/reward heuristic together with a quantized leak adjustment function to vary the leak. Simulation results confirm that the new algorithm can significantly outperform standard LMS when the input eigenvalue spread is high.