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Bibliographic details on Constrained Least Mean Logarithmic Square Algorithm: Design and Performance Analysis.
The so-called constrained least mean-square algorithm is one of the most commonly used linear-equality-constrained adaptive filtering algorithms.
Nov 14, 2017 · This paper introduces a novel constraint adaptive filtering algorithm based on a relative logarithmic cost function which is termed as Constrained Least Mean ...
A new constrained adaptive filtering algorithm, called the constrained least mean p-power error (CLMP) algorithm, which adopts themean p- power error (MPE) ...
The proposed BC-CLMS algorithm shows improved performance with low misalignment, as compared to the CLMS algorithm in a system-identification with noisy input ...
Abstract—The so-called constrained least mean-square algorithm is one of the most commonly used linear-equality- constrained adaptive filtering algorithms.
Missing: Logarithmic | Show results with:Logarithmic
This paper presents the performance analysis of Least Mean Square (LMS) algorithm for adaptive noise cancellation by varying its step size parameter μ for ...
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This paper presents comprehensive theoretical performance analysis of I0-LMS for white Gaussian input data based on some reasonable assumptions, ...
Oct 11, 2021 · In this paper, a family of adaptive filtering algorithms is proposed using hyperbolic sine function (HSF) and inverse hyperbolic sine function (IHSF) function.
The so-called constrained least mean-square algorithm is one of the most commonly used linear-equality-constrained adaptive filtering algorithms.