RT info:eu-repo/semantics/article
T1 Performance study of the application of artificial neural networks to the completion and prediction of data retrieved by underwater sensors
A1 Baladrón García, Carlos
A1 Aguiar Pérez, Javier Manuel
A1 Calavia, Lorena
A1 Carro Martínez, Belén
A1 Sánchez Esguevillas, Antonio Javier
A1 Hernández Callejo, Luis
K1 Artificial intelligence
K1 Artificial Neural Networks (ANN)
K1 Data completion
K1 Data prediction
K1 Underwater sensors
K1 33 Ciencias Tecnológicas
AB This paper presents a proposal for an Artificial Neural Network (ANN)-based architecture for completion and prediction of data retrieved by underwater sensors. Due to the specific conditions under which these sensors operate, it is not uncommon for them to fail, and maintenance operations are difficult and costly. Therefore, completion and prediction of the missing data can greatly improve the quality of the underwater datasets. A performance study using real data is presented to validate the approach, concluding that the proposed architecture is able to provide very low errors. The numbers show as well that the solution is especially suitable for cases where large portions of data are missing, while in situations where the missing values are isolated the improvement over other simple interpolation methods is limited.
PB MDPI
SN 1424-8220
YR 2012
FD 2012
LK https://uvadoc.uva.es/handle/10324/57649
UL https://uvadoc.uva.es/handle/10324/57649
LA eng
NO Sensors, 2012, vol. 12, n. 2, p. 1468-1481
NO Producción Científica
DS UVaDOC
RD 27-nov-2024