• DocumentCode
    2887205
  • Title

    Research on Modeling of Infrared Carbon Monoxide Sensor used in Mine

  • Author

    He, Yu-kai ; Wang, Ru-lin ; Yang, Yu-qiang ; Zhang, Li ; Wang, Jian

  • Author_Institution
    Mech. & Electron; Eng., China Univ. of Mining & Technol., Beijing
  • fYear
    2006
  • fDate
    13-16 Aug. 2006
  • Firstpage
    650
  • Lastpage
    654
  • Abstract
    Several modeling methods based on infrared absorption principle are illustrated and their existing problems are indicated. A new type of infrared carbon monoxide modeling method is presented, the mathematical model was set up by adopting radial basic function´s (RBF) neural network model, a momentum factor´s gradient descending method could be applied to adjust the parameters of RBF neural network. The experimental results show that it can dispel all kinds of influence such as temperature, pressure, humidity, and has a high precision, a strong capacity of anti-jamming, meet the requirements of mine
  • Keywords
    electrochemical sensors; gas sensors; gradient methods; infrared detectors; mining; radial basis function networks; infrared absorption principle; infrared carbon monoxide sensor modeling; mathematical model; momentum factor gradient descending method; radial basic function RBF neural network model; Cybernetics; Electromagnetic wave absorption; Gas detectors; Infrared detectors; Infrared sensors; Infrared spectra; Machine learning; Mathematical model; Neural networks; Optical computing; Statistics; Stochastic processes; Absorption model; Infrared carbon monoxide; Math modeling; RBF neural network;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics, 2006 International Conference on
  • Conference_Location
    Dalian, China
  • Print_ISBN
    1-4244-0061-9
  • Type

    conf

  • DOI
    10.1109/ICMLC.2006.258393
  • Filename
    4028144