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.
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