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        "author": "Alexandru - George Rusu",
        "author_email": "aqrusu@gmail.com",
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            "Natural Language :: English",
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        "description": "# lmso_algorithm\n\nThe least-mean-square (LMS) and the normalized least-mean-square (NLMS) algorithms require a trade-off between fast convergence \nand low misadjustment, obtained by choosing the control parameters. In general, time variable parameters are proposed \naccording to different rules. Many studies on the optimization of the NLMS algorithm imply time variable control parameters \naccording some specific criteria.\n\nThe optimized LMS (LMSO) algorithm [1] for system identification is developed in the context of a state variable model, assuming \nthat the unknown system acts as a time-varying system, following a first-order Markov model [2]. \n\nThe proposed algorithm follows an optimization problem and introduces a variable step-size in order to minimize the system misalignment\n\n\n[1] A. G. Rusu, S. Ciochin\u0103, and C. Paleologu, \u201cOn the step-size optimization of the LMS algorithm,\u201d in Proc. IEEE TSP, 2019, 6 pages.\n\n[2] G. Enzner, H. Buchner, A. Favrot, and F. Kuech, \u201cAcoustic echo control,\u201d in Academic Press Library in Signal Processing, \nvol. 4, ch. 30, pp. 807\u2013877, Academic Press 2014.\n\n\n\n",
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        "keywords": "Adaptive filters,Echo cancellation,System identification",
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