• DocumentCode
    2699541
  • Title

    Ammunition storage reliability forecasting based on radial basis function neural network

  • Author

    Liu, Jiang ; Ling, Dan ; Wang, Song

  • Author_Institution
    Unit 78618, PLA, Chengdu, China
  • fYear
    2012
  • fDate
    15-18 June 2012
  • Firstpage
    599
  • Lastpage
    602
  • Abstract
    In order to forecast ammunition storage reliability better, the paper researched a forecasting method based on neural network which is with the ability of actualizing multi-nonlinear mapping from input to output, and discussed steps of forecasting based on radial basis function (RBF) network. The storage reliability of one new-style ammunition is forecasted based on RBF network. The results show that RBF is suitable for ammunition storage reliability forecasting, and the precision of the train goal is better by using RBF network than by using BP network. RBF network is more suitable for dealing with this problem.
  • Keywords
    military computing; radial basis function networks; reliability; weapons; RBF network; ammunition storage reliability forecasting; multinonlinear mapping; new-style ammunition; radial basis function neural network; Forecasting; Missiles; Neurons; Radial basis function networks; Reliability; Training; Transfer functions; ammunition storage reliability; forecasting; radial basis function neural network;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Quality, Reliability, Risk, Maintenance, and Safety Engineering (ICQR2MSE), 2012 International Conference on
  • Conference_Location
    Chengdu
  • Print_ISBN
    978-1-4673-0786-4
  • Type

    conf

  • DOI
    10.1109/ICQR2MSE.2012.6246305
  • Filename
    6246305