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Robust regularization for normalized LMS algorithms
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DOI：10.1109/TCSII.2006.877280.png)
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We present a novel normalized least mean square (NLMS) algorithm with robust regularization. The proposed algorithm dynamically updates the regularization parameter that is fixed in the conventional epsilon-NLMS algorithms. By exploiting the gradient descent direction we derive a computationally efficient and robust update scheme for the regularization parameter. Through experiments we demonstrate that the proposed algorithm outperforms conventional NLMS algorithms in terms of the convergence rate and the misadjustment error.
Keyword:
normalized gradient
normalized least mean square (NLMS)
regularization parameter
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4.9
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8.8K
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2.5W
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Convergence and steady-state analysis of a variable step-size NLMS algorithm变步长NLMS算法的收敛性和稳态分析.png)
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