• DocumentCode
    536252
  • Title

    The identification research of airplane target based on BP neural network

  • Author

    Yang, Guang ; Zhang, Bai ; Wang, Xiaojuan ; Zhang, Jianfeng ; Pang, Zhenyu ; Li, Hang ; Yang, Xianghua

  • Author_Institution
    Special Profession Dept., Aviation Univ. of Air Force, Changchun, China
  • Volume
    1
  • fYear
    2010
  • fDate
    29-31 Oct. 2010
  • Firstpage
    727
  • Lastpage
    729
  • Abstract
    The airplane goal´s automatic identification is a research hot spot which realizing the target automatic recognition of the remote sensing image. The BP neural network is a multi-layered network which using the non-linear differentiable function to carry on the weight training. It has contained the most essence part in the neural network theory; the BP neural network has obtained the widespread application in the domains of function approach, pattern Identification, information class and data compression because of its simple structure. Identified and researched the type of airplane based on the artificial neural networks method using the MATLAB software. The result indicated: the accuracy of airplane target recognition may achieve 72.1% based on the BP neural network and it can meet the needs.
  • Keywords
    aircraft; backpropagation; data compression; geophysical image processing; image classification; neural nets; object detection; object recognition; remote sensing; BP neural network; MATLAB; airplane goal automatic identification; artificial neural network; data compression; multilayered network; nonlinear differentiable function; pattern Identification; remote sensing image; target recognition; Adaptation model; Atmospheric modeling; Biological system modeling; Computer languages; Image recognition; Mathematical model; Target recognition; Airplane target; BP Neural Network; Network training; Pattern Identification;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Computing and Intelligent Systems (ICIS), 2010 IEEE International Conference on
  • Conference_Location
    Xiamen
  • Print_ISBN
    978-1-4244-6582-8
  • Type

    conf

  • DOI
    10.1109/ICICISYS.2010.5658501
  • Filename
    5658501