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Author: alexgrusu

README.md

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+# lmso_algorithm
+<p align="justify">
+The least-mean-square (LMS) and the normalized least-mean-square (NLMS) algorithms require a trade-off between fast convergence 
+and low misadjustment, obtained by choosing the control parameters. In general, time variable parameters are proposed 
+according to different rules. Many studies on the optimization of the NLMS algorithm imply time variable control parameters 
+according some specific criteria.
+
+The optimized LMS (LMSO) algorithm [1] for system identification is developed in the context of a state variable model, assuming 
+that the unknown system acts as a time-varying system, following a first-order Markov model [2]. 
+
+The proposed algorithm follows an optimization problem and introduces a variable step-size in order to minimize the system misalignment
+
+
+[1] A. G. Rusu, S. Ciochină, and C. Paleologu, “On the step-size optimization of the LMS algorithm,” in Proc. IEEE TSP, 2019, 6 pages.
+
+[2] G. Enzner, H. Buchner, A. Favrot, and F. Kuech, “Acoustic echo control,” in Academic Press Library in Signal Processing, 
+vol. 4, ch. 30, pp. 807–877, Academic Press 2014.
+</p>