A Time-Varying Convergence Parameter for the LMS Algorithm in the Presence of White Gaussian Noise
نویسندگان
چکیده
A novel approach for the least-mean-square (LMS) estimation algorithm is proposed. Rather than using a fixed convergence parameter μ, this approach utilizes a time-varying LMS parameter μn. This technique leads to faster convergence and provides reduced mean-squared error compared to the conventional fixed parameter LMS algorithm. The algorithm has been tested for noise reduction and estimation in narrow-band FM signals corrupted by additive white Gaussian noise.
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