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Comments on 'Convergence and performance analysis of the normalized LMS algorithm with uncorrelated Gaussian data'

机译:关于“具有不相关高斯数据的归一化LMS算法的收敛性和性能分析”的评论

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Noting that a fine analysis is presented for the convergence and misadjustment of the normalized least-mean-square (NLMS) algorithm in the paper by Tarrab and Feuer (see ibid., vol.3, no.4, p.468091, July 1988), the commenter claims that the results and comparisons with the LMS algorithm are not in a form that readily enables the reader to draw practical conclusions. He points out that plotting mean-square error on a linear, instead of logarithmic (dB), scale hides the important detail of the error as it converges to its minimum value, which is exactly the region where the practical engineer requires detailed knowledge to assess performance. Moreover, in the comparison of the NLMS and LMS algorithm convergence rate and misadjustment, the practitioner wants to know how fast the algorithm will converge when the misadjustment is constrained to a specified value.
机译:注意到Tarrab和Feuer在论文中对归一化最小均方(NLMS)算法的收敛和失调进行了精细分析(见同上,第3卷,第4期,第468091页,1988年7月) )的评论者声称,结果和与LMS算法的比较并非以易于使读者得出实用结论的形式存在。他指出,在线性而不是对数(dB)刻度上绘制均方误差会隐藏误差的重要细节,因为误差会收敛到最小值,这正是实际工程师需要详细知识进行评估的区域性能。而且,在NLMS和LMS算法的收敛速度和失调的比较中,从业者想知道当失调被约束到指定值时算法将收敛多快。

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