<?xml version="1.0" encoding="UTF-8"?> <article key="pdf/10003879" mdate="2016-01-03 00:00:00"> <author>Young-Seok Choi</author> <title>Variable Regularization Parameter Normalized Least Mean Square Adaptive Filter</title> <pages>129 - 132</pages> <year>2016</year> <volume>10</volume> <number>1</number> <journal>International Journal of Electrical and Computer Engineering</journal> <ee>https://publications.waset.org/pdf/10003879</ee> <url>https://publications.waset.org/vol/109</url> <publisher>World Academy of Science, Engineering and Technology</publisher> <abstract>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.</abstract> <index>Open Science Index 109, 2016</index> </article>