M.Robinson  Jimenez, S.Oscar  Aviles, Diana M.  Ovalle, 
    Convolutional Neural Network Training for Robotic Applications in
3D Environments,
    Research Journal of Applied Sciences,
    Volume 13,Issue 1,
    2018,
    Pages 41-46,
    ISSN 1815-932x,
    rjasci.2018.41.46,
    (https://makhillpublications.co/view-article.php?doi=rjasci.2018.41.46)
    Abstract: This study presents two training schemes of three deep convolutional neural network architectures
applied to object recognition, based on the depth information supplied for a 3D camera. For this case, the depth
information allows to make the set of training images of each network, its architecture and its characteristics,
generating a dynamic recognition application by variation of the image capture point. The best scheme is
selected to add a weighting layer with saturationn for obtain a final architecture that recognize objects to
different distances with a 91.69% success that mean a maximum error of 8.31%.
    Keywords: Convolutional neural network;robotic applications;3D environment;characteristics;dynamic;information