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Sensors 2012, 12(2), 1468-1481; doi:10.3390/s120201468
Article

Performance Study of the Application of Artificial Neural Networks to the Completion and Prediction of Data Retrieved by Underwater Sensors

1,* , 1
, 1
, 1
, 1
 and 2
1 Universidad de Valladolid, Dpto. TSyCeIT, ETSIT, Paseo de Belén 15, 47011 Valladolid, Spain 2 CIEMAT, Autovía de Navarra A15, salida 56, Lubia, 42290 Soria, Spain
* Author to whom correspondence should be addressed.
Received: 27 December 2011 / Revised: 23 January 2012 / Accepted: 30 January 2012 / Published: 2 February 2012
(This article belongs to the Special Issue Underwater Sensor Nodes and Underwater Sensor Networks)
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Abstract

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.
Keywords: artificial intelligence; artificial neural networks; data completion; data prediction; underwater sensors artificial intelligence; artificial neural networks; data completion; data prediction; underwater sensors
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Baladrón, C.; Aguiar, J.M.; Calavia, L.; Carro, B.; Sánchez-Esguevillas, A.; Hernández, L. Performance Study of the Application of Artificial Neural Networks to the Completion and Prediction of Data Retrieved by Underwater Sensors. Sensors 2012, 12, 1468-1481.

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