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https://ri-ng.uaq.mx/handle/123456789/8905Registro completo de metadatos
| Campo DC | Valor | Lengua/Idioma |
|---|---|---|
| dc.rights.license | http://creativecommons.org/licenses/by-nc-nd/4.0 | es_ES |
| dc.contributor | Marco Antonio Aceves Fernández | es_ES |
| dc.creator | Marco Antonio Olguin Sánchez | es_ES |
| dc.date | 2024-04-22 | - |
| dc.date.accessioned | 2023-08-02T16:29:33Z | - |
| dc.date.available | 2023-08-02T16:29:33Z | - |
| dc.date.issued | 2024-04-22 | - |
| dc.identifier.uri | https://ri-ng.uaq.mx/handle/123456789/8905 | - |
| dc.description | Forecasting air pollution is a challenging problem today that requires special attention in large cities since they are home to millions of people who are at risk of respiratory diseases every day. At the same time, there has been exponential growth in the research and application of deep learning, which is useful to treat temporary data such as pollution levels, leaving aside the physical and chemical characteristics of the particles and only focusing on predicting the next levels of contamination. This work seeks to contribute to society by presenting a useful way to optimize recurrent neural networks of the short and long-term memory type through a statistical process (Gaussian processes) for the correct optimization of the processes. | es_ES |
| dc.format | Adobe PDF | es_ES |
| dc.language.iso | spa | es_ES |
| dc.publisher | Ingeniería | es_ES |
| dc.relation.requires | No | es_ES |
| dc.rights | En Embargo | es_ES |
| dc.subject | Ingeniería y Tecnología | es_ES |
| dc.subject | Ciencias Tecnológicas | es_ES |
| dc.subject | Ciencia de los ordenadores | es_ES |
| dc.title | Red Neuronal Recurrente de Memoria a Largo y Corto Plazo Optimizada por Procesos Gaussianos para Predecir la Contaminación Atmosférica | es_ES |
| dc.type | Tesis de licenciatura | es_ES |
| dc.creator.tid | curp | es_ES |
| dc.creator.identificador | OUSM990422HMCLNR01 | es_ES |
| dc.contributor.role | Director | es_ES |
| dc.degree.name | Ingeniería Física | es_ES |
| dc.degree.department | Facultad de Ingeniería | es_ES |
| dc.degree.level | Licenciatura | es_ES |
| Aparece en las colecciones: | Ingeniería Física | |
Ficheros en este ítem:
| Fichero | Descripción | Tamaño | Formato | |
|---|---|---|---|---|
| IGLIN-243663.pdf | 2.27 MB | Adobe PDF |  Visualizar/Abrir |
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