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人工神经网络在水下传感器采集数据的完成和预测中的应用性能研究。

Performance study of the application of Artificial Neural Networks to the completion and prediction of data retrieved by underwater sensors.

机构信息

Universidad de Valladolid, Dpto. TSyCeIT, ETSIT, Paseo de Belén 15, 47011 Valladolid, Spain.

出版信息

Sensors (Basel). 2012;12(2):1468-81. doi: 10.3390/s120201468. Epub 2012 Feb 2.

DOI:10.3390/s120201468
PMID:22438720
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3304122/
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.

摘要

本文提出了一种基于人工神经网络 (ANN) 的架构，用于完成和预测水下传感器获取的数据。由于这些传感器的工作条件特殊，它们经常会出现故障，而且维护操作既困难又昂贵。因此，对缺失数据的补充和预测可以极大地提高水下数据集的质量。本文通过实际数据进行了性能研究，验证了该方法的有效性，结论表明，所提出的架构能够提供非常低的误差。这些数据还表明，该解决方案特别适用于大量数据缺失的情况，而在缺失值孤立的情况下，其改进效果相对于其他简单的插值方法是有限的。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/028e/3304122/88f90b76757e/sensors-12-01468f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/028e/3304122/a6381c4f0690/sensors-12-01468f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/028e/3304122/efab0d82c2d1/sensors-12-01468f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/028e/3304122/44e59b226e5e/sensors-12-01468f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/028e/3304122/8d8ecbd183c8/sensors-12-01468f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/028e/3304122/88f90b76757e/sensors-12-01468f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/028e/3304122/a6381c4f0690/sensors-12-01468f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/028e/3304122/efab0d82c2d1/sensors-12-01468f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/028e/3304122/44e59b226e5e/sensors-12-01468f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/028e/3304122/8d8ecbd183c8/sensors-12-01468f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/028e/3304122/88f90b76757e/sensors-12-01468f5.jpg

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