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Robust incremental normalized least mean square algorithm with variable step sizes over distributed networks.
- Source :
-
Signal Processing . Mar2018, Vol. 144, p1-6. 6p. - Publication Year :
- 2018
-
Abstract
- An improved incremental normalized least mean square (INLMS) algorithm is developed by minimizing the Huber cost function, which is robust against impulsive noises, over distributed networks. To significantly suppress impulsive noises, a recursive scheme based on the incremental cooperation strategy is designed for updating the cutoff parameter in the Huber function. Since the proposed algorithm can be interpreted as a variable step size INLMS algorithm, it has faster convergence rate and lower steady-state error than some existing incremental distributed algorithms in both impulsive and non-impulsive noise environments. In addition, to track a sudden change of the unknown system, a modified method of resetting the cutoff parameter is developed. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 01651684
- Volume :
- 144
- Database :
- Academic Search Index
- Journal :
- Signal Processing
- Publication Type :
- Academic Journal
- Accession number :
- 126438109
- Full Text :
- https://doi.org/10.1016/j.sigpro.2017.09.016