The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
A robust variable step-size LMS-type algorithm: analysis and simulations
Abstract
A number of time-varying step-size algorithms have been proposed to enhance the performance of the conventional LMS algorithm. Experimentation with these algorithms indicates that their performance is highly sensitive to the noise disturbance. This paper presents a robust variable step-size LMS-type algorithm providing fast convergence at early stages of adaptation while ensuring small final misadjustment. The performance of the algorithm is not affected by existing uncorrelated noise disturbances. An approximate analysis of convergence and steady-state performance for zero-mean stationary Gaussian inputs and for nonstationary optimal weight vector is provided. Simulation results comparing the proposed algorithm to current variable step-size algorithms clearly indicate its superior performance for cases of stationary environments. For nonstationary environments, our algorithm performs as well as other variable step-size algorithms in providing performance equivalent to that of the regular LMS algorithm.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Haykin, S. (1986) · Cited by 12,603
Rogers, S. (1996) · Control Engineering Practice · doi:10.1016/0967-0661(96)82838-3 · Cited by 9,278
Stationary and nonstationary learning characteristics of the LMS adaptive filter
Widrow, B., McCool, J. M., Larimore, M. G., & Johnson, C. R. (1976) · Proceedings of the IEEE · doi:10.1109/proc.1976.10286 · Cited by 1,268
Stationary and Nonstationary Learning Characteristics of the LMS Adaptive Filter
Widrow, B., McCool, J., Larimore, M. G., & Johnson, C. R. (1977) · Aspects of Signal Processing · doi:10.1007/978-94-010-1223-2_23 · Cited by 1,195
A variable step size LMS algorithm
Kwong, R. H., & Johnston, E. W. (1992) · IEEE Transactions on Signal Processing · doi:10.1109/78.143435 · Cited by 1,097
A stochastic gradient adaptive filter with gradient adaptive step size
Mathews, V. J., & Xie, Z. (1993) · IEEE Transactions on Signal Processing · doi:10.1109/78.218137 · Cited by 390
Performance advantage of complex LMS for controlling narrow-band adaptive arrays
Horowitz, L. L., & Senne, K. D. (1981) · IEEE Transactions on Acoustics Speech and Signal Processing · doi:10.1109/tassp.1981.1163602 · Cited by 165
Adaptive algorithms with an automatic gain control feature
Shan, T., & Kailath, T. (1988) · IEEE Transactions on Circuits and Systems · doi:10.1109/31.1709 · Cited by 131
Adaptive filters with individual adaptation of parameters
Mikhael, W. B., Wu, F., Kazovsky, L. G., Kang, G. S., & Fransen, L. (1986) · IEEE Transactions on Circuits and Systems · doi:10.1109/tcs.1986.1085982 · Cited by 117
Cited by
The Quaternion LMS Algorithm for Adaptive Filtering of Hypercomplex Processes
Took, C. C., & Mandic, D. P. (2009) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2008.2010600 · Cited by 332
Metadata
- Authors
- Aboulnasr, T., & Mayyas, K.
- Venue
- IEEE Transactions on Signal Processing
- Published
- 1997-03
- Volume
- 45
- Issue
- 3
- Pages
- 631-639
- ISSN
- 1053-587X (ISSN-L)
- OpenAlex
- W2115947173
- Cited by
- 732
- References
- 9
- FWCI
- 9.23
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A robust variable step-size LMS-type algorithm: analysis and simulations. https://app.cochat.ai/papers/10.1109/78.558478
- The paper
- Aboulnasr, T., & Mayyas, K. (1997). A robust variable step-size LMS-type algorithm: analysis and simulations. IEEE Transactions on Signal Processing. https://doi.org/10.1109/78.558478
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
