Abstract
Convergence rate of the normalized least mean square adaptive estimation algorithm is analyzed. An approximation of the adaptation coefficient value which yields the best convergence speed is derived in function of the input correlation. Results concerning the trade-off : convergence speed versus asymptotic mean square estimation error are explored to substract practical rules for the adjustment of the adaptation coefficient for best possible estimation performance within a limited time delay. These results are particularly interesting for the diagnosis and adaptive control schemes which are essentially based on real-time identification.
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