• DocumentCode
    2591687
  • Title

    The Research of urban road tunnel longitudinal ventilation control based on RBF neural network

  • Author

    Du, Pengying ; Luo, Xiaoping

  • Author_Institution
    Key Lab. of Intell. Syst., Zhejiang Univ. City Coll., Hangzhou, China
  • Volume
    2
  • fYear
    2010
  • fDate
    28-31 Aug. 2010
  • Firstpage
    85
  • Lastpage
    87
  • Abstract
    The urban road tunnel longitudinal ventilation system has strong time-varying, non1inear and large delay characteristics and it is difficult to get the precise mathematical model, Conventional linear control theories are inefficient. RBF (Radial Basis Function) neural network is adopted in urban road tunnel longitudinal ventilation control system considering traffic flow, CO (Carbon monoxide), VI (Visibility) values and other factors. In normal traffic flow, density traffic flow, sparse traffic flow different situations for tunnel ventilation, test simulation results show that this control method is better and it is more efficient than the conventional method of nearly 15%.
  • Keywords
    geotechnical engineering; neurocontrollers; nonlinear control systems; radial basis function networks; road traffic; structural engineering; tunnels; density traffic flow; linear control theories; radial basis function neural network; sparse traffic flow; urban road tunnel longitudinal ventilation control system; Artificial neural networks; Fans; Intelligent control; Radial basis function networks; Roads; RBF; energy conservation; traffic flow; urban road tunnel; ventilation control;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Geoscience and Remote Sensing (IITA-GRS), 2010 Second IITA International Conference on
  • Conference_Location
    Qingdao
  • Print_ISBN
    978-1-4244-8514-7
  • Type

    conf

  • DOI
    10.1109/IITA-GRS.2010.5603278
  • Filename
    5603278