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A class of adaptively regularised PNLMS algorithms
Beth Jelfs
*
, Danilo P. Mandic
, Jacob Benesty
*
Corresponding author for this work
Research output
:
Chapter in Book/Report/Conference proceeding
›
Conference contribution
13
Citations (Scopus)
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Keyphrases
Normalized Least Mean Square
100%
Normalized Least Mean Square Algorithm
100%
Enhanced Stability
50%
Performance Enhancement
50%
Steady-state Performance
50%
Computational Complexity
50%
Performance Stability
50%
Non-stationary Environments
50%
Adaptive Regularization Parameter
50%
Global Regularization
50%
Adaptive Regularization
50%
Input Dynamics
50%
Sparse Environment
50%
Regularization Factor
50%
Computer Science
Least-Mean-Square Algorithm
100%
Regularization
50%
Computational Complexity
25%
Regularization Parameter
25%
Performance State
25%
Engineering
Least Mean Square
100%
Regularization
50%
Computational Complexity
25%
Regularization Parameter
25%
Dynamic Input
25%
Mathematics
Mean Square
100%
Regularization
75%