Red Neuronal Recurrente de Memoria a Largo y Corto Plazo Optimizada por Procesos Gaussianos para Predecir la Contaminación Atmosférica

Marco Antonio Olguin Sánchez

Buscar

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