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.