An Optimized LMS Algorithm
Project 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.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for lmso_algorithm-1.2-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 90e3cc4e62146505dedff6ff80eef5b048806e191685c17e5b5ea649bdb3a14b |
|
MD5 | d13c03e9fc78803a18edef0557bc29c7 |
|
BLAKE2b-256 | 4fc8625fdfdfb2797524ea2125aecfada054a5cbf311de8f9df9d095d76b986e |