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A novel approach to introducing adaptive filters based on the LMS algorithm and its variants

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posted on 14.01.2010, 12:51 by Emilio Soria, Javier Calpe, Jonathon Chambers, Marcelino Martinez, Gustavo Camps, José David Martin Guerrero
This paper presents a new approach to introducing adaptive filters based on the least-mean-square (LMS) algorithm and its variants in an undergraduate course on digital signal processing. Unlike other filters currently taught to undergraduate students, these filters are nonlinear and time variant. This proposal introduces adaptive filtering in the context of a linear time-invariant system using a real problem. In this way, introducing adaptive filters using concepts already familiar to the students motivates their interest through practical application. The key point for this simplification is that the input to the filter is constant so that the adaptive filter becomes linear. Therefore, a complete arsenal of mathematical tools, already known by the students, is available to analyze the performance of the filters and obtain the key parameters to adaptive filters, e.g., speed of convergence and stability. Several variants of the basic LMS algorithm are described the same way.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Citation

SORIA, E. ... et al, 2004. A novel approach to introducing adaptive filters based on the LMS algorithm and its variants. IEEE Transactions on Education, 47 (1), pp. 127-133

Publisher

© IEEE

Version

VoR (Version of Record)

Publication date

2004

Notes

This is an article from the journal, IEEE Transactions on Education [© IEEE]. It is also available at: http://ieeexplore.ieee.org/ Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

ISSN

0018-9359

Language

en