Metadata-Version: 1.0
Name: lmso-algorithm
Version: 1.1
Summary: An Optimized LMS Algorithm
Home-page: https://github.com/alexgrusu/lmso_algorithm
Author: Alexandru - George Rusu
Author-email: aqrusu@gmail.com
License: UNKNOWN
Description: # lmso_algorithm
        
        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.
        
        
Keywords: Adaptive filters,Echo cancellation,System identification
Platform: UNKNOWN
