Combined LMS/F Algorithm

Author

Shao Jen Lim
J. G. Harris, Missouri University of Science and TechnologyFollow

Abstract

A new adaptive filter algorithm has been developed that combines the benefits of the least mean square (LMS) and least mean fourth (LMF) methods. This algorithm, called LMS/F, outperforms the standard LMS algorithm judging either constant convergence rate or constant misadjuslment. While LMF outperforms LMS for certain noise profiles, its stability cannot be guaranteed for known input signals even for very small step sizes. However, both LMS and LMS/F have good stability properties and LMS/F only adds a few more computations per iteration compared to LMS. Simulations of a non-stationary system identification problem demonstrate the performance benefits of the LMS/F algorithm.

Recommended Citation

S. J. Lim and J. G. Harris, "Combined LMS/F Algorithm," Electronics Letters, vol. 33, no. 6, pp. 467 - 468, Wiley; Institution of Engineering and Technology (IET), Mar 1997.

The definitive version is available at https://doi.org/10.1049/el:19970311

Department(s)

Electrical and Computer Engineering

Keywords and Phrases

Adaptive filters; Least mean squares methods

International Standard Serial Number (ISSN)

0013-5194

Document Type

Article - Journal

Document Version

Final Version

File Type

text

Language(s)

English

Rights

© 2025 The Authors, All rights reserved.

Publication Date

13 Mar 1997