A new approach to online training for the Fuzzy ARTMAP artificial neural network

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorIFSP- Federal Institute of São Paulo-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorSantos-Junior, Carlos R.-
Autor(es): dc.creatorAbreu, Thays-
Autor(es): dc.creatorLopes, Mara L.M.-
Autor(es): dc.creatorLotufo, Anna D.P.-
Data de aceite: dc.date.accessioned2025-08-21T19:21:48Z-
Data de disponibilização: dc.date.available2025-08-21T19:21:48Z-
Data de envio: dc.date.issued2022-04-29-
Data de envio: dc.date.issued2022-04-29-
Data de envio: dc.date.issued2021-11-30-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.asoc.2021.107936-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/229705-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/229705-
Descrição: dc.descriptionThe 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.-
Descrição: dc.descriptionUniversidade Estadual Paulista-
Descrição: dc.descriptionIFSP- Federal Institute of São Paulo Campus Hortolândia, Av. Thereza Ana Cecon Breda 1896 - CEP: 13183-091-
Descrição: dc.descriptionElectrical Engineering Department Campus of Ilha Solteira Unesp - Univ Estadual Paulista, Av. Brasil 56–PO Box 31 - CEP: 15385-000Ilha Solteira-
Descrição: dc.descriptionElectrical Engineering Department Campus of Ilha Solteira Unesp - Univ Estadual Paulista, Av. Brasil 56–PO Box 31 - CEP: 15385-000Ilha Solteira-
Idioma: dc.languageen-
Relação: dc.relationApplied Soft Computing-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectArtificial neural networks-
Palavras-chave: dc.subjectContinuous training-
Palavras-chave: dc.subjectFuzzy ARTMAP-
Palavras-chave: dc.subjectOnline learning-
Título: dc.titleA new approach to online training for the Fuzzy ARTMAP artificial neural network-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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