• DocumentCode
    480219
  • Title

    The Fluorescence Spectroscopy Recognition of the Mineral Oil Based on the Multiresolution Orthogonal Multiwavelet Neural Network

  • Author

    Jiangtao, Lv ; Yutian, Wang ; Zhao, Pan

  • Author_Institution
    Meas. Technol. & Instrum. Key Lab. of Hebei Province, Yanshan Univ., Qin Huangdao
  • Volume
    4
  • fYear
    2008
  • fDate
    12-14 Dec. 2008
  • Firstpage
    785
  • Lastpage
    787
  • Abstract
    The singular value eigenvectors of the different kinds of the mineral oil stylebooks are obtained by parameterizing the three-dimensional fluorescence spectroscopy. They are complicated and not easy to be recognized by the simple formula. The multiwavelet neural network is introduced to realize the identification of the different kinds of the mineral oil. It was layered. It had the feature of the part study. The prompting function of the network is constructed by the multiscale function and multiwavelet function. The experiment indicates that the network has all the virtue of the wavelet neural network (WNN). It also has the much better approach property than the WNN. It can effectively recognize the fine distinction between the different spectrums and realize the identification of the oil by much fewer train times than the WNN.
  • Keywords
    eigenvalues and eigenfunctions; fluorescence spectroscopy; mineral processing industry; neural nets; oils; petroleum industry; singular value decomposition; wavelet transforms; fluorescence spectroscopy recognition; mineral oil stylebooks; multiresolution orthogonal multiwavelet neural network; multiscale function; singular value eigenvectors; Biological neural networks; Computer science; Convergence; Fluorescence; Minerals; Neural networks; Petroleum; Software engineering; Software measurement; Spectroscopy; fluorescence spectroscopy; mineral oil; multiwavelet neural network; spectral recognition;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Science and Software Engineering, 2008 International Conference on
  • Conference_Location
    Wuhan, Hubei
  • Print_ISBN
    978-0-7695-3336-0
  • Type

    conf

  • DOI
    10.1109/CSSE.2008.999
  • Filename
    4722736