新的变步长归一化最小均方算法

为了解决最小均方 (L east Mean Square,L MS)算法收敛速度和稳态误差之间的矛盾 ,提出了一种新的变步长归一化 (Norm alized) L MS(NL MS)算法。这种算法根据滤波器系数的梯度计算新的步长。当算法尚未收敛时 ,使用较大的步长 ;随着收敛程度的加深 ,逐渐减小步长。试验显示了该算法具有很好的收敛性能和跟踪性能。与其它的变步长L MS算法相比 ,该算法在标准 NL MS算法基础上增加的运算量和存储量都很少且与阶数无关 ;而且该算法的参数受观测噪声的影响很小 ,在观测噪声强度发生变化的情况下不需要重新调整参数 ,仍然可以保持很好的收敛性能

Novel variable step size NLMS algorithm

A novel variable step size (normalized least mean square, NLMS) algorithm is proposed in where the gradient of the filter weights is used to adjust the step size. During convergence, a decrease in the gradient of the filter weights causes a step size decrease. The computer simulations show very good convergence ability and tracing ability. Compared with other variable step size algorithms, the proposed algorithm uses little additional memory and computations relative to the original NLMS and the addition work is independent of the filter length. The other advantage is that the coefficients of the proposed algorithm are insensitive to the additive noise energy so that even if the noise energy changes, the algorithm can still work well without re adjusting the coefficients.