• DocumentCode
    315186
  • Title

    Neural network isolation of system inputs for transient modelling and control

  • Author

    Tascillo, PhD Anya

  • Author_Institution
    Res. Lab., Ford Motor Co., Dearborn, MI, USA
  • Volume
    2
  • fYear
    1997
  • fDate
    9-12 Jun 1997
  • Firstpage
    718
  • Abstract
    A neural network is used to predict the sensitivity of a complex nonlinear system (such as an automobile) to input variation, which will aid greatly in the effort to model the system and the effects of changes to its controllers. A blend of signal processing techniques is used to provide maximum resolution neural network inputs for various drivers, vehicles, engine technologies, transmissions, velocity traces, and operating temperatures. The neural net predicts what four different vehicle outputs will be, given a sample of driving inputs
  • Keywords
    automobiles; large-scale systems; mechanical engineering computing; neural nets; nonlinear systems; sensitivity analysis; signal processing; transient response; automobile; complex nonlinear system; input isolation; neural network; sensitivity analysis; signal processing; transient modelling; Automobiles; Engines; Neural networks; Nonlinear control systems; Nonlinear systems; Predictive models; Signal processing; Signal resolution; Temperature; Vehicle driving;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks,1997., International Conference on
  • Conference_Location
    Houston, TX
  • Print_ISBN
    0-7803-4122-8
  • Type

    conf

  • DOI
    10.1109/ICNN.1997.616110
  • Filename
    616110