This paper studies the mean and mean square convergence behaviors of the normalized least mean square (NLMS) algorithm with Gaussian inputs and additive white Gaussian noise. Using the Price's theorem and the framework proposed by Bershad in IEEE Transactions on Acoustics, Speech, and Signal Processing (1986, 1987), new expressions for the excess mean square error, stability bound and decoupled difference equations describing the mean and mean square convergence behaviors of the NLMS algorithm using the generalized Abelian integral functions are derived. These new expressions which closely resemble those of the LMS algorithm allow us to interpret the convergence performance of the NLMS algorithm in Gaussian environment. The theoretical anal...
In this work, a family of normalized least mean fourth algorithms is presented. Unlike the LMF algor...
Adaptive filters that self-adjust their transfer functions according to optimizing algorithms are po...
Least mean square (LMS) is a widely used steepest descent algorithm known with efficient tracking ab...
This paper studies the convergence analysis of the least mean M-estimate (LMM) and normalized least ...
This paper studies the convergence behaviors of the normalized least mean square (NLMS) and the norm...
Shows that the NLMS (normalized least-mean-square) algorithm is a potentially faster converging algo...
This paper studies the convergence performance of the transform domain normalized least mean square ...
The Normalized Least Mean Square (NLMS) algorithm is an important variant of the classical LMS algor...
This paper compares the convergence rate performance of the Normalized Least-Mean-Square (or NLMS) a...
This paper studies the convergence behaviors of the noise-constrained normalized least mean squares ...
DoctorAdaptive filters that self-adjust their transfer functions according to optimization algorithm...
Abstract-This paper analyzes the performance of the GP- called GP-NLMS algorithm, which has the lowe...
# The Author(s) 2010. This article is published with open access at Springerlink.com Abstract This p...
AbstractBy building the generalized Sigmoid function relationship between normalized step-size and t...
This paper analyzes the performance of the GP-NLMS algorithm, revealing the nature of its fast conve...
In this work, a family of normalized least mean fourth algorithms is presented. Unlike the LMF algor...
Adaptive filters that self-adjust their transfer functions according to optimizing algorithms are po...
Least mean square (LMS) is a widely used steepest descent algorithm known with efficient tracking ab...
This paper studies the convergence analysis of the least mean M-estimate (LMM) and normalized least ...
This paper studies the convergence behaviors of the normalized least mean square (NLMS) and the norm...
Shows that the NLMS (normalized least-mean-square) algorithm is a potentially faster converging algo...
This paper studies the convergence performance of the transform domain normalized least mean square ...
The Normalized Least Mean Square (NLMS) algorithm is an important variant of the classical LMS algor...
This paper compares the convergence rate performance of the Normalized Least-Mean-Square (or NLMS) a...
This paper studies the convergence behaviors of the noise-constrained normalized least mean squares ...
DoctorAdaptive filters that self-adjust their transfer functions according to optimization algorithm...
Abstract-This paper analyzes the performance of the GP- called GP-NLMS algorithm, which has the lowe...
# The Author(s) 2010. This article is published with open access at Springerlink.com Abstract This p...
AbstractBy building the generalized Sigmoid function relationship between normalized step-size and t...
This paper analyzes the performance of the GP-NLMS algorithm, revealing the nature of its fast conve...
In this work, a family of normalized least mean fourth algorithms is presented. Unlike the LMF algor...
Adaptive filters that self-adjust their transfer functions according to optimizing algorithms are po...
Least mean square (LMS) is a widely used steepest descent algorithm known with efficient tracking ab...