Add to ORKGThe normalized least mean fourth (NLMF) algorithm is presented in this work and shown to have potentially faster convergence. Unlike the LMF algorithm, the convergence behavior of the NLMF algorithm is independent of the input data correlation statistics. Sufficient conditions for the NLMF algorithm convergence in the mean are obtained and the analysis of the steady-state performance is carried out using the feedback approach. Simulation results confirm the performance of the NLMF algorith
In this work, the convergence analysis of the variable weight mixed-norm LMS-LMF (least mean squares...
In this work, a novel algorithm, called the signed regressor least mean fourth (SRLMF) adaptive algo...
In this work, the ε-normalized sign regressor least mean mixed-norm (NSRLMMN) adaptive algorithm is ...
The normalized least mean fourth (NLMF) algorithm is presented in this work and shown to have potent...
The normalized least mean fourth (NLMF) algorithm is presented in this work and shown to have potent...
The normalized least mean-fourth (NLMF) algorithm is presented in this work and shown to have potent...
In this work, a family of normalized least mean fourth algo-rithms is presented. Unlike the LMF algo...
In this work, the convergence and tracking behavior of the ε-normalized sign regressor least mean fo...
In this paper, we provide some insights into the convergence and steady-state behaviors of the sign-...
In this paper, a new algorithm, the ∈-normalized sign regressor least mean fourth (NSRLMF) algorithm...
This paper compares the convergence rate performance of the Normalized Least-Mean-Square (or NLMS) a...
Shows that the NLMS (normalized least-mean-square) algorithm is a potentially faster converging algo...
Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, p...
This paper studies the convergence behaviors of the normalized least mean square (NLMS) and the norm...
A novel algorithm, called the signed regressor least mean fourth (SRLMF) adaptive algorithm, that re...
In this work, the convergence analysis of the variable weight mixed-norm LMS-LMF (least mean squares...
In this work, a novel algorithm, called the signed regressor least mean fourth (SRLMF) adaptive algo...
In this work, the ε-normalized sign regressor least mean mixed-norm (NSRLMMN) adaptive algorithm is ...
The normalized least mean fourth (NLMF) algorithm is presented in this work and shown to have potent...
The normalized least mean fourth (NLMF) algorithm is presented in this work and shown to have potent...
The normalized least mean-fourth (NLMF) algorithm is presented in this work and shown to have potent...
In this work, a family of normalized least mean fourth algo-rithms is presented. Unlike the LMF algo...
In this work, the convergence and tracking behavior of the ε-normalized sign regressor least mean fo...
In this paper, we provide some insights into the convergence and steady-state behaviors of the sign-...
In this paper, a new algorithm, the ∈-normalized sign regressor least mean fourth (NSRLMF) algorithm...
This paper compares the convergence rate performance of the Normalized Least-Mean-Square (or NLMS) a...
Shows that the NLMS (normalized least-mean-square) algorithm is a potentially faster converging algo...
Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, p...
This paper studies the convergence behaviors of the normalized least mean square (NLMS) and the norm...
A novel algorithm, called the signed regressor least mean fourth (SRLMF) adaptive algorithm, that re...
In this work, the convergence analysis of the variable weight mixed-norm LMS-LMF (least mean squares...
In this work, a novel algorithm, called the signed regressor least mean fourth (SRLMF) adaptive algo...
In this work, the ε-normalized sign regressor least mean mixed-norm (NSRLMMN) adaptive algorithm is ...