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

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1 Author(s)
Morgan, D.R. ; AT&T Bell Lab., Whippany, NJ, USA

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.<>

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Information Theory, IEEE Transactions on  (Volume:35 ,  Issue: 6 )