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Forecasting SO2 Air Pollution in Salamanca, Mexico using an ADALINE
Cita
Vega Corona, Antonio, Andina de la Fuente, Diego 
ORCID: https://orcid.org/0000-0001-7036-2646, Barron Adame, Jose Miguel, Cortina Januchs, María Guadalupe and Mendoza, Us
(2008).
Forecasting SO2 Air Pollution in Salamanca, Mexico using an ADALINE.
En: "4th Virtual International Conference on Intelligent Production Machines and Systems, IPROMS 2008", 01/07/2008-14/07/2008, Cardiff University, UK. ISBN 978-1-904445-81-4.
ORCID: https://orcid.org/0000-0001-7036-2646, Barron Adame, Jose Miguel, Cortina Januchs, María Guadalupe and Mendoza, Us
(2008).
Forecasting SO2 Air Pollution in Salamanca, Mexico using an ADALINE.
En: "4th Virtual International Conference on Intelligent Production Machines and Systems, IPROMS 2008", 01/07/2008-14/07/2008, Cardiff University, UK. ISBN 978-1-904445-81-4.
Descripción
| Título: | Forecasting SO2 Air Pollution in Salamanca, Mexico using an ADALINE |
|---|---|
| Autor/es: |
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| Tipo de Documento: | Ponencia en Congreso o Jornada (Artículo) |
| Título del Evento: | 4th Virtual International Conference on Intelligent Production Machines and Systems, IPROMS 2008 |
| Fechas del Evento: | 01/07/2008-14/07/2008 |
| Lugar del Evento: | Cardiff University, UK |
| Título del Libro: | Proceedings on Intelligent Production Machines and Systems |
| Fecha: | 2008 |
| ISBN: | 978-1-904445-81-4 |
| Materias: | |
| ODS: | |
| Escuela: | E.T.S.I. Telecomunicación (UPM) |
| Departamento: | Señales, Sistemas y Radiocomunicaciones |
| Licencias Creative Commons: | Reconocimiento - Sin obra derivada - No comercial |
Resumen
A comparison between a linear regression model and a Non-linear regressionmodel is presented in this work for forecasting of pollution levels due to SO2 in Salamanca city, Gto. Prediction is performed by means of an Adaptive Linear Neural Network (ADALINE) and a Generalized Regression NeuralNetwork (GRNN). Prediction experiments are realized for 1, 12 and 24 hours in advance, and the results for linear regression have been satisfactory. The performance estimation of both models are determined using the Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Obtained results are compared. The final results indicated that ADALINE outperforms the past approach using GRNN.
Más información
| ID de Registro: | 4267 |
|---|---|
| Identificador DC: | https://oa.upm.es/4267/ |
| Identificador OAI: | oai:oa.upm.es:4267 |
| URL Oficial: | http://www.iproms.org/og |
| Depositado por: | Memoria Investigacion |
| Depositado el: | 20 Sep 2010 12:03 |
| Ultima Modificación: | 20 Abr 2016 13:33 |
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