Convergence and steady-state analysis of the normalized least mean fourth algorithm
Digital Signal Processing
A noise constrained least mean fourth (NCLMF) adaptive algorithm
Signal Processing
Computational advantages of reverberating loops for sensorimotor learning
Neural Computation
Digital Signal Processing
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It is demonstrated that the normalized least mean square (NLMS) algorithm can be viewed as a modification of the widely used LMS algorithm. The NLMS is shown to have an important advantage over the LMS, which is that its convergence is independent of environmental changes. In addition, the authors present a comprehensive study of the first and second-order behavior in the NLMS algorithm. They show that the NLMS algorithm exhibits significant improvement over the LMS algorithm in convergence rate, while its steady-state performance is considerably worse