Next Article in Journal
MAPSkew: Metaheuristic Approaches for Partitioning Skew in MapReduce
Previous Article in Journal
Adaptive Operator Quantum-Behaved Pigeon-Inspired Optimization Algorithm with Application to UAV Path Planning
Previous Article in Special Issue
Steady-State Performance of an Adaptive Combined MISO Filter Using the Multichannel Affine Projection Algorithm
Article Menu

Export Article

Open AccessArticle
Algorithms 2019, 12(1), 4; https://doi.org/10.3390/a12010004

On Fast Converging Data-Selective Adaptive Filtering

1
Signals, Multimedia, and Telecommunications Lab., Universidade Federal do Rio de Janeiro DEL/Poli &PEE/COPPE/UFRJ, P.O. Box 68504, Rio de Janeiro RJ 21941-972, Brazil
2
SnT-Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg,4365 Luxembourg City, Luxembourg
3
Tadeu N. Ferreira, Fluminense Federal University, Engineering School, R. Passo da Patria, 156, Room E-406, Niteroi RJ 24210-240, Brazi
*
Author to whom correspondence should be addressed.
Received: 30 November 2018 / Revised: 17 December 2018 / Accepted: 18 December 2018 / Published: 21 December 2018
(This article belongs to the Special Issue Adaptive Filtering Algorithms)
Full-Text   |   PDF [1175 KB, uploaded 25 December 2018]   |  

Abstract

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. View Full-Text
Keywords: adaptive signal processing; adaptive filters; parameter estimation; system identification; equalization; prediction; learning systems; data processing; LMS-Newton; conjugate gradient adaptive signal processing; adaptive filters; parameter estimation; system identification; equalization; prediction; learning systems; data processing; LMS-Newton; conjugate gradient
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Mendonça, M.O.K.; Ferreira, J.O.; Tsinos, C.G.; Diniz, P.S.R.; Ferreira, T.N. On Fast Converging Data-Selective Adaptive Filtering. Algorithms 2019, 12, 4.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Algorithms EISSN 1999-4893 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top