A new approach to online training for the Fuzzy ARTMAP artificial neural network
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1016/j.asoc.2021.107936 http://hdl.handle.net/11449/229705 |
Resumo: | The evolution of internet resources has led to an increase in the flow of data and, consequently, the need for classification or forecasting models that support online learning. The Fuzzy ARTMAP neural network has been used in the most areas of knowledge; however, few have explored real-time applications that require continuous training. In this work, a Fuzzy ARTMAP neural network with continuous training is proposed. This new network can acquire knowledge via classification or prediction. Modifications made to the architecture and learning algorithm enable online learning from the first sample of data and perform the classification or forecasting at any time during training. To validate the proposed model, three experiments were performed, one for forecasting and two for classification. Each experiment used benchmark databases and compared its final results with the results of the original Fuzzy ARTMAP neural network. The results demonstrate the ability of the proposed model to acquire knowledge from the first data samples in a stable and efficient way. Thus, this study contributes to the evolution of the Fuzzy ARTMAP neural network and introduces the continuous training method as an effective alternative to real-time applications. |
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Repositório Institucional da UNESP |
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spelling |
A new approach to online training for the Fuzzy ARTMAP artificial neural networkArtificial neural networksContinuous trainingFuzzy ARTMAPOnline learningThe evolution of internet resources has led to an increase in the flow of data and, consequently, the need for classification or forecasting models that support online learning. The Fuzzy ARTMAP neural network has been used in the most areas of knowledge; however, few have explored real-time applications that require continuous training. In this work, a Fuzzy ARTMAP neural network with continuous training is proposed. This new network can acquire knowledge via classification or prediction. Modifications made to the architecture and learning algorithm enable online learning from the first sample of data and perform the classification or forecasting at any time during training. To validate the proposed model, three experiments were performed, one for forecasting and two for classification. Each experiment used benchmark databases and compared its final results with the results of the original Fuzzy ARTMAP neural network. The results demonstrate the ability of the proposed model to acquire knowledge from the first data samples in a stable and efficient way. Thus, this study contributes to the evolution of the Fuzzy ARTMAP neural network and introduces the continuous training method as an effective alternative to real-time applications.Universidade Estadual PaulistaIFSP- Federal Institute of São Paulo Campus Hortolândia, Av. Thereza Ana Cecon Breda 1896 - CEP: 13183-091Electrical Engineering Department Campus of Ilha Solteira Unesp - Univ Estadual Paulista, Av. Brasil 56–PO Box 31 - CEP: 15385-000Ilha SolteiraElectrical Engineering Department Campus of Ilha Solteira Unesp - Univ Estadual Paulista, Av. Brasil 56–PO Box 31 - CEP: 15385-000Ilha SolteiraIFSP- Federal Institute of São PauloUniversidade Estadual Paulista (UNESP)Santos-Junior, Carlos R.Abreu, Thays [UNESP]Lopes, Mara L.M. [UNESP]Lotufo, Anna D.P. [UNESP]2022-04-29T08:35:25Z2022-04-29T08:35:25Z2021-12-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1016/j.asoc.2021.107936Applied Soft Computing, v. 113.1568-4946http://hdl.handle.net/11449/22970510.1016/j.asoc.2021.1079362-s2.0-85117110887Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengApplied Soft Computinginfo:eu-repo/semantics/openAccess2024-07-04T19:06:25Zoai:repositorio.unesp.br:11449/229705Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T18:58:42.273089Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
A new approach to online training for the Fuzzy ARTMAP artificial neural network |
title |
A new approach to online training for the Fuzzy ARTMAP artificial neural network |
spellingShingle |
A new approach to online training for the Fuzzy ARTMAP artificial neural network Santos-Junior, Carlos R. Artificial neural networks Continuous training Fuzzy ARTMAP Online learning |
title_short |
A new approach to online training for the Fuzzy ARTMAP artificial neural network |
title_full |
A new approach to online training for the Fuzzy ARTMAP artificial neural network |
title_fullStr |
A new approach to online training for the Fuzzy ARTMAP artificial neural network |
title_full_unstemmed |
A new approach to online training for the Fuzzy ARTMAP artificial neural network |
title_sort |
A new approach to online training for the Fuzzy ARTMAP artificial neural network |
author |
Santos-Junior, Carlos R. |
author_facet |
Santos-Junior, Carlos R. Abreu, Thays [UNESP] Lopes, Mara L.M. [UNESP] Lotufo, Anna D.P. [UNESP] |
author_role |
author |
author2 |
Abreu, Thays [UNESP] Lopes, Mara L.M. [UNESP] Lotufo, Anna D.P. [UNESP] |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
IFSP- Federal Institute of São Paulo Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
Santos-Junior, Carlos R. Abreu, Thays [UNESP] Lopes, Mara L.M. [UNESP] Lotufo, Anna D.P. [UNESP] |
dc.subject.por.fl_str_mv |
Artificial neural networks Continuous training Fuzzy ARTMAP Online learning |
topic |
Artificial neural networks Continuous training Fuzzy ARTMAP Online learning |
description |
The evolution of internet resources has led to an increase in the flow of data and, consequently, the need for classification or forecasting models that support online learning. The Fuzzy ARTMAP neural network has been used in the most areas of knowledge; however, few have explored real-time applications that require continuous training. In this work, a Fuzzy ARTMAP neural network with continuous training is proposed. This new network can acquire knowledge via classification or prediction. Modifications made to the architecture and learning algorithm enable online learning from the first sample of data and perform the classification or forecasting at any time during training. To validate the proposed model, three experiments were performed, one for forecasting and two for classification. Each experiment used benchmark databases and compared its final results with the results of the original Fuzzy ARTMAP neural network. The results demonstrate the ability of the proposed model to acquire knowledge from the first data samples in a stable and efficient way. Thus, this study contributes to the evolution of the Fuzzy ARTMAP neural network and introduces the continuous training method as an effective alternative to real-time applications. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-12-01 2022-04-29T08:35:25Z 2022-04-29T08:35:25Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1016/j.asoc.2021.107936 Applied Soft Computing, v. 113. 1568-4946 http://hdl.handle.net/11449/229705 10.1016/j.asoc.2021.107936 2-s2.0-85117110887 |
url |
http://dx.doi.org/10.1016/j.asoc.2021.107936 http://hdl.handle.net/11449/229705 |
identifier_str_mv |
Applied Soft Computing, v. 113. 1568-4946 10.1016/j.asoc.2021.107936 2-s2.0-85117110887 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Applied Soft Computing |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808129006941241344 |