File(s) under permanent embargo
New variable step-sizes minimizing mean-square deviation for the lms-type algorithms
Version 2 2024-06-05, 05:40Version 2 2024-06-05, 05:40
Version 1 2015-03-11, 09:38Version 1 2015-03-11, 09:38
journal contribution
posted on 2024-06-05, 05:40 authored by S Zhao, DL Jones, Sui Yang KhooSui Yang Khoo, Z ManThe least-mean-square-type (LMS-type) algorithms are known as simple and effective adaptation algorithms. However, the LMS-type algorithms have a trade-off between the convergence rate and steady-state performance. In this paper, we investigate a new variable step-size approach to achieve fast convergence rate and low steady-state misadjustment. By approximating the optimal step-size that minimizes the mean-square deviation, we derive variable step-sizes for both the time-domain normalized LMS (NLMS) algorithm and the transform-domain LMS (TDLMS) algorithm. The proposed variable step-sizes are simple quotient forms of the filtered versions of the quadratic error and very effective for the NLMS and TDLMS algorithms. The computer simulations are demonstrated in the framework of adaptive system modeling. Superior performance is obtained compared to the existing popular variable step-size approaches of the NLMS and TDLMS algorithms. © 2014 Springer Science+Business Media New York.
History
Journal
Circuits, systems, and signal processingVolume
33Pagination
2251-2265Location
Berlin, GermanyPublisher DOI
ISSN
0278-081XeISSN
1531-5878Language
engPublication classification
C Journal article, C1 Refereed article in a scholarly journalCopyright notice
2014, SpringerIssue
7Publisher
SpringerUsage metrics
Categories
Keywords
ConvergenceDiscrete transformsLeast mean square algorithmsMean-square errorScience & TechnologyTechnologyEngineering, Electrical & ElectronicEngineeringADAPTIVE ALGORITHMNLMS ALGORITHMFILTERSPERFORMANCE090601 Circuits and Systems970109 Expanding Knowledge in EngineeringSchool of Engineering090609 Signal Processing
Licence
Exports
RefWorksRefWorks
BibTeXBibTeX
Ref. managerRef. manager
EndnoteEndnote
DataCiteDataCite
NLMNLM
DCDC