As previously shown, the normalized least mean square (NLMS)algorithm has superior convergence properties than the least meansquare(LMS) algorithm. However, the weight noise effect of the NLMS algorithm is large so that the steady state residue power islarger than that for the LMS algorithm. A generalized NLMSalgorithm is developed based upon the pseudoinverse of anestimated covariance matrix. A preliminary evaluation indicatesimproved performance can be attained but the implementationcomplexity might be high.


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    Title :

    A Generalization of the Normalized LMS Algorithm


    Contributors:

    Published in:

    Publication date :

    1987-07-01


    Size :

    637292 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





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