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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-12-01
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| Series: | Algorithms |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1999-4893/12/1/4 |
| _version_ | 1817997941889040384 |
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| author | Marcele O. K. Mendonça Jonathas O. Ferreira Christos G. Tsinos Paulo S R Diniz Tadeu N. Ferreira |
| author_facet | Marcele O. K. Mendonça Jonathas O. Ferreira Christos G. Tsinos Paulo S R Diniz Tadeu N. Ferreira |
| author_sort | Marcele O. K. Mendonça |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-14T02:45:41Z |
| format | Article |
| id | doaj.art-689b9444a14a47808c9ce86a1d9051cf |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-04-14T02:45:41Z |
| publishDate | 2018-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-689b9444a14a47808c9ce86a1d9051cf2022-12-22T02:16:31ZengMDPI AGAlgorithms1999-48932018-12-01121410.3390/a12010004a12010004On Fast Converging Data-Selective Adaptive FilteringMarcele O. K. Mendonça0Jonathas O. Ferreira1Christos G. Tsinos2Paulo S R Diniz3Tadeu N. Ferreira4Signals, 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, BrazilSignals, 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, BrazilSnT-Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg,4365 Luxembourg City, LuxembourgSignals, 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, BrazilTadeu N. Ferreira, Fluminense Federal University, Engineering School, R. Passo da Patria, 156, room E-406,24210-240 Niteroi RJ, BraziThe 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.http://www.mdpi.com/1999-4893/12/1/4adaptive signal processingadaptive filtersparameter estimationsystem identificationequalizationpredictionlearning systemsdata processingLMS-Newtonconjugate gradient |
| spellingShingle | Marcele O. K. Mendonça Jonathas O. Ferreira Christos G. Tsinos Paulo S R Diniz Tadeu N. Ferreira On Fast Converging Data-Selective Adaptive Filtering Algorithms adaptive signal processing adaptive filters parameter estimation system identification equalization prediction learning systems data processing LMS-Newton conjugate gradient |
| title | On Fast Converging Data-Selective Adaptive Filtering |
| title_full | On Fast Converging Data-Selective Adaptive Filtering |
| title_fullStr | On Fast Converging Data-Selective Adaptive Filtering |
| title_full_unstemmed | On Fast Converging Data-Selective Adaptive Filtering |
| title_short | On Fast Converging Data-Selective Adaptive Filtering |
| title_sort | on fast converging data selective adaptive filtering |
| topic | adaptive signal processing adaptive filters parameter estimation system identification equalization prediction learning systems data processing LMS-Newton conjugate gradient |
| url | http://www.mdpi.com/1999-4893/12/1/4 |
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