| 基于改进NLMS算法的自适应滤波算法 |
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| 引用本文： | 简献忠,刘树明,李菲,张晗.基于改进NLMS算法的自适应滤波算法[J].微计算机信息,2012(5):27-28,115. |
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| 作者姓名： | 简献忠 刘树明 李菲 张晗 |
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| 作者单位： | 上海理工大学光电信息与计算机工程学院 |
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| 摘 要： | 最小均方算法是应用最广泛的自适应算法之一,但其收敛速度欠佳。在传统NLMS算法的基础上,提出了重复调整归一化最小均方算法(DRNLMS)即在相邻两输入信号样本的间隔时间进行额外调整运算,以提高算法的收敛性,并通过计算机仿真实现该算法。
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| 关 键 词： | 自适应滤波 最小均方算法 收敛性 |
Adaptive filtering algorithm based on improved NLMS adaptive algorithm |
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| JIAN Xian-zhong,LIU Shu-ming,LI Fei,ZHANG Han.Adaptive filtering algorithm based on improved NLMS adaptive algorithm[J].Control & Automation,2012(5):27-28,115. |
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| Authors: | JIAN Xian-zhong LIU Shu-ming LI Fei ZHANG Han |
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| Affiliation: | (School of Optical-Electrical and computer engineering, University of Shanghai for Science and Technology, Shanghai 200093, China) |
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| Abstract: | Least mean square algorithm is one of the most widely used adaptive algorithms in adaptive filtering, but its poor convergence performance. On the basis of traditional NLMS algorithm, Data-reusing normalized least mean square algorithm (DRNLMS) was put forward, i.e. input signal samples in adjacent two additional adjustment time interval of computing, to improve convergence performance, and realize this algorithm through computer simulation. |
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| Keywords: | adaptive filtering least mean square algorithm convergence performance |
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