Modeling of Particulate Pollutants Using a Memory-Based Recurrent Neural Network Implemented on an FPGA

The present work describes the training and subsequent implementation on an FPGA board of an LSTM neural network for the modeling and prediction of the exceedances of criteria pollutants such as nitrogen dioxide (NO(2)), carbon monoxide (CO), and particulate matter (PM(10) and PM(2.5)). Understandin...

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Detalles Bibliográficos
Autores principales: Ramírez-Montañez, Julio Alberto, Rangel-Magdaleno, Jose de Jesús, Aceves-Fernández, Marco Antonio, Ramos-Arreguín, Juan Manuel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10537238/
https://www.ncbi.nlm.nih.gov/pubmed/37763967
http://dx.doi.org/10.3390/mi14091804

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10537238/
https://www.ncbi.nlm.nih.gov/pubmed/37763967
http://dx.doi.org/10.3390/mi14091804

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