• DocumentCode
    2624895
  • Title

    Research of Neural Network Control on Ignition Spark Angle of Vehicle Electronic-Control Gasoline Engine

  • Author

    Suyun, Luo ; Yuanming, Gong ; Changshui, Wu

  • Author_Institution
    Shanghai Univ. of Eng. Sci., Shanghai, China
  • Volume
    2
  • fYear
    2011
  • fDate
    6-7 Jan. 2011
  • Firstpage
    1081
  • Lastpage
    1083
  • Abstract
    Correct ignition spark angle is important to enhance the dynamic performance and economy, reduce exhaust emission for electronic-control gasoline engine. Ignition Spark Angle is affected by many factors and characterized with dispersivity, nonlinear and uncertainty. Based on electronic-control engine pedestal experiments, this article tried to obtain the ignition spark angles by back propagation neural network method. The built neural network model composites multiple factors, was proved to be effectively and accurately to obtain the Ignition spark angles.
  • Keywords
    air pollution; backpropagation; ignition; internal combustion engines; neural nets; neurocontrollers; sparks; back propagation neural network; dynamic performance; electronic control gasoline engine; exhaust emission reduction; ignition spark angle; pedestal experiments; Artificial neural networks; Biological neural networks; Ignition; Interpolation; Neurons; Sparks; Back Propagation Neural Network; Electronic-Control Gasoline Engine; Ignition Spark Angle;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Measuring Technology and Mechatronics Automation (ICMTMA), 2011 Third International Conference on
  • Conference_Location
    Shangshai
  • Print_ISBN
    978-1-4244-9010-3
  • Type

    conf

  • DOI
    10.1109/ICMTMA.2011.552
  • Filename
    5721378