2026-05-05T22:03:36Zhttps://uvadoc.uva.es/oai/request
oai:uvadoc.uva.es:10324/576492025-02-13T09:20:19Zcom_10324_1191com_10324_931com_10324_894col_10324_1379
UVaDOC author Baladrón García, Carlos author Aguiar Pérez, Javier Manuel author Calavia, Lorena author Carro Martínez, Belén author Sánchez Esguevillas, Antonio Javier author Hernández Callejo, Luis 2022-12-02T12:30:11Z 2022-12-02T12:30:11Z 2012 Sensors, 2012, vol. 12, n. 2, p. 1468-1481 1424-8220 https://uvadoc.uva.es/handle/10324/57649 10.3390/s120201468 1468 2 1481 Sensors 12 1424-8220 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. eng Attribution 3.0 Unported Performance study of the application of artificial neural networks to the completion and prediction of data retrieved by underwater sensors info:eu-repo/semantics/article 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URL https://uvadoc.uva.es/bitstream/10324/57649/1/Performance-study-application.pdf File MD5 c0a5bdad94b9dd5254bb182f5dbeec6c 226864 application/pdf Performance-study-application.pdf