H∞ optimality and a posteriori output estimate of the forgetting factor NLMS algorithm
H∞ optimality and a posteriori output estimate of the forgetting factor NLMS algorithm
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DOI：
10.1016/j.automatica.2016.09.025
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发表时间：
2017-01-01
期刊：
影响因子：
6.4
通讯作者：
Nishiyama, Kiyoshi
中科院分区：
文献类型：
--
作者：
Nishiyama, Kiyoshi
归一化最小均方(NLMS)算法被广泛应用于自适应滤波。可以使用各种权重参数来扩展NLMS算法，以提高其性能。一种这样的扩展涉及使用H无穷框架将遗忘因子适当地引入到NLMS算法中。由此得到的遗忘因子NLMS(FFNLMS)算法可视为改进比例NLMS(IPNLMS)算法的特例。这项工作表明，FFNLMS算法是H无穷最优的，并且后验输出估计在足够大的时间内与观测信号相同。(C)2016爱思唯尔有限公司。保留所有权利。
The normalized least mean squares (NLMS) algorithm is widely used for adaptive filtering. The NLMS algorithm may be extended using a variety of weight parameters that improve its performance. One such extension involves appropriately introducing a forgetting factor into the NLMS algorithm using the H-infinity framework. The resultant forgetting factor NLMS (FFNLMS) algorithm may be regarded as a special case of the improved proportionate NLMS (IPNLMS) algorithm. This work reveals that the FFNLMS algorithm is H-infinity-optimaL and the a posteriori output estimate is identical to the observation signal for sufficiently large times. (C) 2016 Elsevier Ltd. All rights reserved.