Supervised metaplasticity for big data: application to pollutant concentrations forecast

Fombellida Vetas, Juan and Alarcón Mondejar, Martín Javier and Torres Alegre, Santiago and Andina de la Fuente, Diego (2017). Supervised metaplasticity for big data: application to pollutant concentrations forecast. In: "International Work-Conference on the Interplay Between Natural and Artificial Computation (IWINAC 2017)", 19/06/2018 - 23/06/2018, La Coruña, Spain. pp. 374-383.

Description

Title: Supervised metaplasticity for big data: application to pollutant concentrations forecast
Author/s:
  • Fombellida Vetas, Juan
  • Alarcón Mondejar, Martín Javier
  • Torres Alegre, Santiago
  • Andina de la Fuente, Diego
Item Type: Presentation at Congress or Conference (Article)
Event Title: International Work-Conference on the Interplay Between Natural and Artificial Computation (IWINAC 2017)
Event Dates: 19/06/2018 - 23/06/2018
Event Location: La Coruña, Spain
Title of Book: Biomedical Applications Based on Natural and Artificial Computing
Date: 2017
Subjects:
Freetext Keywords: Metaplasticity Big Data Plasticity MLP AMP Pollutant concentration Artificial neural network
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Otro
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Artificial Metaplasticity Multilayer Perceptron is a training algorithm implementation for Artificial Neural Networks inspired in biological metaplasticity property of neurons and Shannon’s information theory. It is based on the hypothesis that a higher amount of information from a Data Set is included in the most atypical data. Using this theory basis a supervised algorithm is developed giving more relevance to the less frequent patterns and subtracting relevance to the more frequent ones. This algorithm has achieved deeper learning on several mutidisciplinar data sets without the need of a Deep Network. The application of this algorithm to a key nowadays environmental problem: the pollutant concentrations prediction in cities, is now considered. The city selected is Salamanca, Mexico, that has been ranked as one of the most polluted cities in the world. The concerning registered pollutants are particles in the order of 10 μm or less (PM10). The prediction of concentrations of those pollutants can be a powerful tool in order to take preventive measures such as the reduction of emissions and alerting the affected population. In this paper the results obtained are compared with previous recent published algorithms for the prediction of the pollutant concentration. Discussed and conclusions are presented.

More information

Item ID: 50322
DC Identifier: http://oa.upm.es/50322/
OAI Identifier: oai:oa.upm.es:50322
Official URL: https://link.springer.com/chapter/10.1007/978-3-319-59773-7_38
Deposited by: Memoria Investigacion
Deposited on: 23 Oct 2018 15:09
Last Modified: 23 Oct 2018 15:09
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