Variable Regularization Parameter Normalized Least Mean Square Adaptive Filter
نویسنده
چکیده
We present a normalized LMS (NLMS) algorithm with robust regularization. Unlike conventional NLMS with the fixed regularization parameter, the proposed approach dynamically updates the regularization parameter. By exploiting a gradient descent direction, we derive a computationally efficient and robust update scheme for the regularization parameter. In simulation, we demonstrate the proposed algorithm outperforms conventional NLMS algorithms in terms of convergence rate and misadjustment error. Keywords—Regularization, normalized LMS, system identification, robustness.
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