입력에 따라 스텝사이즈 업데이트를 조정하는 NLMS 알고리즘

Abstract

This paper presents a variable step-size normalized least-mean-square (VSS-NLMS) algorithm using only one parameter. Numerous VSS-NLMS algorithms lead to fast convergence rate and low misadjustment depend on several parameters that are not simple to tune on practice. The proposed VSS-NLMS algorithm leads to faster convergence rate and low misadjustment though the algorithm uses only one parameter. And the parameter can easily tuned through input data.