Skip to main content

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


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

lmso_algorithm-1.2.tar.gz (4.2 kB view hashes)

Uploaded Source

Built Distribution

lmso_algorithm-1.2-py3-none-any.whl (17.6 kB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page