In this work, a Tensor LMF algorithm which is designed by minimizing the Mean-Fourth-Error (MFE) criterion using the tensor factorization. We also provide the ...
This algorithm is designed by employing separability of linear operators on the standard. LMS and by minimizing the standard stochastic gradient-based.
In this work, a Tensor LMF algorithm which is designed by minimizing the Mean-Fourth-Error (MFE) criterion using the tensor factorization. We also provide the ...
The normalized least-mean-fourth (NLMF) adaptive filter is an extension of the LMF adaptive filter (Least-mean-fourth (LMF)).
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Apr 5, 2024 · In this study, a highly efficient spectral-Galerkin method is posed for the fourth-order Steklov equation with boundary eigenvalue. By making ...
In this paper we consider the steepest descent LMS. (least mean square) algorithm of Widrow and Hoff which is probably the simplest and most widely used. In ...
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This contribution focuses on the separability of linear operators, a typical property of interest when dealing with tensors, and shows that a gradient type ...
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The proposed algorithm obtained least mean square deviation compared to existing second and fourth order algorithms.
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The least-mean-fourth (LMF) adaptive filter implemented according to the paper. The LMF filter can be created as follows where n is the size (number of taps) ...
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The least-mean fourth (LMF) algorithm is well-known to provide fast convergence and lower steady-state error, especially in non-Gaussian noise environments in ...