Abstract
The performance of the least-mean-square (LMS) algorithm is governed by its step-size parameter. In this paper, we present a family of optimized LMS-based algorithms (in terms of the step-size control), in the context of system identification. A time-variant system model is considered, and the optimization criterion is based on the minimization of the system misalignment. Simulations performed in the context of acoustic echo cancellation indicate that these algorithms achieve a proper compromise in terms of fast convergence/tracking and low mis adjustment.
Recommended Citation
S. Ciochinǎ et al., "A Family of Optimized LMS-Based Algorithms for System Identification," European Signal Processing Conference, pp. 1803 - 1807, article no. 7760559, Institute of Electrical and Electronics Engineers, Nov 2016.
The definitive version is available at https://doi.org/10.1109/EUSIPCO.2016.7760559
Department(s)
Electrical and Computer Engineering
International Standard Book Number (ISBN)
978-099286265-7
International Standard Serial Number (ISSN)
2219-5491
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
28 Nov 2016
