Application of a Fuzzy ARTMAP Neural Network for Indoor Air Quality Prediction

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversity of Limerick-
Autor(es): dc.creatorFerreira, Willian De Assis Pedrobon-
Autor(es): dc.creatorGrout, Ian-
Autor(es): dc.creatorSilva, Alexandre Cesar Rodrigues da[UNESP]-
Data de aceite: dc.date.accessioned2025-08-21T18:31:41Z-
Data de disponibilização: dc.date.available2025-08-21T18:31:41Z-
Data de envio: dc.date.issued2023-03-01-
Data de envio: dc.date.issued2023-03-01-
Data de envio: dc.date.issued2021-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/iEECON53204.2022.9741563-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/239874-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/239874-
Descrição: dc.descriptionIndoor air quality monitoring is an important activity to ensure continued health and well-being of citizens living, studying, and working in indoor environments. This practice has been widely developed through the application of low-cost sensors that are able to measure gas concentrations, particulate matter, and other components such as humidity and temperature that affect indoor air quality. Additionally, machine learning algorithms have been applied in the interpretation of sampled environmental data to improve the performance of monitoring systems. This paper proposes the implementation of a fuzzy ARTMAP neural network, which employs the concepts of Adaptive Resonance Theory (ART), to compute the prediction of particulate matter sampled in a domestic bedroom environment. With the application of a specialized online training architecture, the fuzzy ARTMAP network can be a promising alternative to predict particulate matter time series data modeled in sliding windows, obtaining predictions 24-hour ahead with mean absolute error (MAE) ranging here from 0.26 to 7.65.-
Descrição: dc.descriptionSão Paulo State University Department of Electrical Engineering-
Descrição: dc.descriptionUniversity of Limerick Department of Electronic and Computer Engineering-
Descrição: dc.descriptionSão Paulo State University Department of Electrical Engineering-
Idioma: dc.languageen-
Relação: dc.relationProceedings of the 2022 International Electrical Engineering Congress, iEECON 2022-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectfuzzy ARTMAP neural network-
Palavras-chave: dc.subjectindoor air quality-
Palavras-chave: dc.subjectonline training-
Palavras-chave: dc.subjectparticulate matter prediction-
Título: dc.titleApplication of a Fuzzy ARTMAP Neural Network for Indoor Air Quality Prediction-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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