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

被引:20
|
作者
Baladron, Carlos [1 ]
Aguiar, Javier M. [1 ]
Calavia, Lorena [1 ]
Carro, Belen [1 ]
Sanchez-Esguevillas, Antonio [1 ]
Hernandez, Luis [2 ]
机构
[1] Univ Valladolid, Dpto TSyCeIT, ETSIT, E-47011 Valladolid, Spain
[2] CIEMAT, Lubia 42290, Soria, Spain
关键词
artificial intelligence; artificial neural networks; data completion; data prediction; underwater sensors;
D O I
10.3390/s120201468
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
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.
引用
收藏
页码:1468 / 1481
页数:14
相关论文
共 50 条
  • [41] Artificial neural networks for the performance prediction of large solar systems
    Kalogirou, S. A.
    Mathioulakis, E.
    Belessiotis, V.
    RENEWABLE ENERGY, 2014, 63 : 90 - 97
  • [42] Performance Prediction of Solar Collectors Using Artificial Neural Networks
    Xie, Hui
    Liu, Li
    Ma, Fei
    Fan, Huifang
    2009 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND COMPUTATIONAL INTELLIGENCE, VOL II, PROCEEDINGS, 2009, : 573 - 576
  • [43] Prediction of paste backfill performance using artificial neural networks
    Rankine, Rudd M.
    Sivakugan, Nagaratnam
    PROCEEDINGS OF THE 16TH INTERNATIONAL CONFERENCE ON SOIL MECHANICS AND GEOTECHNICAL ENGINEERING, VOLS 1-5: GEOTECHNOLOGY IN HARMONY WITH THE GLOBAL ENVIRONMENT, 2005, : 1107 - 1110
  • [44] Architecture performance prediction using evolutionary artificial neural networks
    Castillo, P. A.
    Mora, A. M.
    Merelo, J. J.
    Laredo, J. L. J.
    Moreto, M.
    Cazorla, F. J.
    Valero, M.
    Mckee, S. A.
    APPLICATIONS OF EVOLUTIONARY COMPUTING, PROCEEDINGS, 2008, 4974 : 175 - +
  • [45] Prediction of displacement in Reinforced concrete based on artificial neural networks using sensors
    sivasuriyan A.
    Vijayan D.S.
    Measurement: Sensors, 2023, 27
  • [46] Application of artificial neural network for prediction of operational performance of MNSR
    Tabas, E. Jangjoo
    Nejad, M. Zaidabadi
    Mokhtari, J.
    Abbassi, Y.
    NUCLEAR ENGINEERING AND DESIGN, 2024, 419
  • [47] Application of artificial neural network in prediction of the combine harvester performance
    Gundoshmian, Tarahom Mesri
    Ghassemzadeh, Hamid Reza
    Abdollahpour, Shamsollah
    Navid, Hossein
    JOURNAL OF FOOD AGRICULTURE & ENVIRONMENT, 2010, 8 (02): : 721 - 724
  • [48] STUDY ON TURBOMACHINERY PERFORMANCE PREDICTION WITH NEURAL NETWORKS
    Fan Huiyuan
    Xi Guang
    Wang Shangjin (SER Turbomachinery Research Center School of Power and Energy Engineering
    Chinese Journal of Mechanical Engineering(English Edition), 2000, (01) : 52 - 57
  • [49] Study on turbomachinery performance prediction with neural networks
    Huiyuan, Fan, 2000, Chin Mech Eng Soc, Beijing (13):
  • [50] The use of artificial neural networks in completion stimulation and design
    Shelley, B
    Stephenson, S
    COMPUTERS & GEOSCIENCES, 2000, 26 (08) : 941 - 951