The Extended Feature LMS Algorithm: Exploiting Hidden Sparsity for Systems with Unknown Spectrum-Reference-Cited by-同舟云学术

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The Extended Feature LMS Algorithm: Exploiting Hidden Sparsity for Systems with Unknown Spectrum

Published:2020-06-07 Issue:1 Volume:40 Page:174-192
ISSN:0278-081X
Container-title:Circuits, Systems, and Signal Processing
language:en
Short-container-title:Circuits Syst Signal Process

Author:

Yazdanpanah Hamed^ORCID,Apolinário José A.^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Signal Processing

Link

https://link.springer.com/content/pdf/10.1007/s00034-020-01461-3.pdf

Reference38 articles.

1. M.S. Aslam, M.A.Z. Raja, A new adaptive strategy to improve online secondary path modeling in active noise control systems using fractional signal processing approach. Signal Process. 107, 433–443 (2015)

2. J. Benesty, S.L. Gay, An improved PNLMS algorithm, in Proceedings IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May, Dallas, USA (2002), pp. 1881–1884

3. Z.A. Bhotto, A. Antoniou, A family of shrinkage adaptive-filtering algorithms. IEEE Trans. Signal Process. 61, 1689–1697 (2013)

4. N.I. Chaudhary, S. Zubair, M.S. Aslam et al., Design of momentum fractional LMS for Hammerstein nonlinear system identification with application to electrically stimulated muscle model. Eur. Phys. J. Plus 134, 407 (2019)

5. N.I. Chaudhary, S. Zubair, M.A.Z. Raja et al., Normalized fractional adaptive methods for nonlinear control autoregressive systems. Appl. Math. Model. 66, 457–471 (2019)

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1. Improved feature least mean square algorithm;International Journal of Adaptive Control and Signal Processing;2022-11-17

2. Robust and sparsity-aware adaptive filters: A Review;Signal Processing;2021-12

3. Sparse least mean fourth adaptive algorithm for censored regression;Twelfth International Conference on Signal Processing Systems;2021-01-20

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