改进最小化准则的改进归一化最小均方算法

2011 International Conference on Computational Intelligence and Communication Networks Pub Date : 2011-10-07 DOI:10.1109/CICN.2011.116

Manish D. Sawale, Ram Narayan Yadav

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引用次数: 0

摘要

本文利用过去的权重向量和自适应学习率，提出了一种改进的归一化最小均方(NLMS)算法的最小化准则。该准则最小化了当前更新的权重向量和过去的权重向量之间的差的每个平方欧氏范数的总和。在不同信噪比下，改进的NLMS算法比传统的NLMS算法具有更低的误差。仿真结果表明，随着原权向量和信噪比的增大，所提NLMS算法的收敛速度加快。

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Modified Normalized Least Mean Square Algorithm with Improved Minimization Criterion

In this paper we develop an improved minimization criterion for normalized least mean squares (NLMS) algorithm using past weight vectors and adaptive learning rate. The proposed criterion minimizes the summation of each squared Euclidean norm of difference between the currently updated weight vector and past weight vector. The result of the modified NLMS algorithm has lower misalignment than the conventional NLMS algorithm for various SNR. The simulation shows that the convergence rate of proposed NLMS algorithm is faster as the previous weight vectors and SNR increases.

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2011 International Conference on Computational Intelligence and Communication Networks

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