文件名称:A-nonparametric-variable-step-size-NLMS-algorithm
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A nonparametric adaptive filtering approach is proposed in this paper. The algorithm is
obtained by exploiting a time-varying step size in the traditional NLMS weight update
equation. The step size is adjusted according to the square of a time-averaging estimate
of the autocorrelation of a priori and a posteriori error. Therefore, the new algorithm has
more effective sense proximity to the optimum solution independent of uncorrelated measurement
noise. Moreover, this algorithm has fast convergence at the early stages of adaptation
and small final misadjustment at steady-state process. It works reliably and is easy
to implement since the update function is nonparametric. Furthermore, the experimental
results in system identification applications are presented to illustrate the principle and
efficiency of the proposed algorithm.
obtained by exploiting a time-varying step size in the traditional NLMS weight update
equation. The step size is adjusted according to the square of a time-averaging estimate
of the autocorrelation of a priori and a posteriori error. Therefore, the new algorithm has
more effective sense proximity to the optimum solution independent of uncorrelated measurement
noise. Moreover, this algorithm has fast convergence at the early stages of adaptation
and small final misadjustment at steady-state process. It works reliably and is easy
to implement since the update function is nonparametric. Furthermore, the experimental
results in system identification applications are presented to illustrate the principle and
efficiency of the proposed algorithm.
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A nonparametric variable step-size NLMS algorithm for transversal filters.pdf