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一种改进的归一化LMS算法 被引量:9

An Improved Normalized LMS Algorithm
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摘要 传统的归一化最小二乘(LMS)算法可以解决算法收敛速度和采样信号收敛误差之间的矛盾。通过对收敛步长的归一化处理,从而减小收敛过程的稳态误差。但是,这种算法却将收敛的步长变为固定,不利于算法的快速收敛。为此,提出一种改进的归一化LMS算法,将Sigmoid函数变步长最小均方(SVSLMS)算法与归一化LMS算法结合,提高了归一化LMS算法的收敛速度。仿真结果表明,该算法性能较之归一化LMS算法、SVSLMS算法和普通LMS算法均更优异。 The traditional normalized least mean square (LMS) algorithm can solve the contradiction between convergence speed of the algorithm and convergence error of the sampled signals. By nor- malizing the convergence step-size, the steady-state error of convergence process is reduced. But this algorithm turns the convergence step-size into fixed value, which is unfavorable to fast conver- gence of the algorithm. Thus this paper proposes an improved normalized LMS algorithm, which combines the Sigmoid variable step-size least mean square (SVSLMS) algorithm with thenormal- ized LMS algorithm,improves the convergence speed of normalized LMS algorithm. Simulation re- suits show that the performance of the proposed algorithm is better than that of normalized LMS algorithm,SVSLMS algorithm and ordinary LMS algorithm.
作者 杨坡 刘铸华 YANG Po LIU Zhu-hua(The 723 Institute of CSIC,Yangzhou 225001 ,China)
出处 《舰船电子对抗》 2017年第4期59-61,65,共4页 Shipboard Electronic Countermeasure
关键词 归一化最小二乘算法 Sigmoid函数变步长最小均方算法 收敛速度 normalized least mean square algorithm Sigmoid variable step-size least mean square algorithm convergence speed
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