On Fast Converging Data-Selective Adaptive Filtering

The amount of information currently generated in the world has been increasing exponentially, raising the question of whether all acquired data is relevant for the learning algorithm process. If a subset of the data does not bring enough innovation, data-selection strategies can be employed to reduc...

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Bibliographic Details
Published in:Algorithms 2019-01, Vol.12 (1), p.4
Main Authors: Mendonça, Marcele O. K., Ferreira, Jonathas O., Tsinos, Christos G., Diniz, Paulo S R, Ferreira, Tadeu N.
Format: Article
Language:English
Subjects:
Adaptive algorithms
Adaptive filters
adaptive signal processing
Algorithms
conjugate gradient
Convergence
Data acquisition
data processing
equalization
learning systems
LMS-Newton
Machine learning
Noise
parameter estimation
prediction
system identification
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Summary:The amount of information currently generated in the world has been increasing exponentially, raising the question of whether all acquired data is relevant for the learning algorithm process. If a subset of the data does not bring enough innovation, data-selection strategies can be employed to reduce the computational complexity cost and, in many cases, improve the estimation accuracy. In this paper, we explore some adaptive filtering algorithms whose characteristic features are their fast convergence and data selection. These algorithms incorporate a prescribed data-selection strategy and are compared in distinct applications environments. The simulation results include both synthetic and real data.
ISSN:1999-4893
1999-4893
DOI:10.3390/a12010004