• DocumentCode
    322910
  • Title

    Feature-level signal processing for odor sensor arrays

  • Author

    Roppel, T. ; Dunman, K. ; Padgett, M. ; Wilson, D. ; Lindblad, T.

  • Author_Institution
    Dept. of Electr. Eng., Auburn Univ., AL, USA
  • Volume
    1
  • fYear
    1997
  • fDate
    9-14 Nov 1997
  • Firstpage
    218
  • Abstract
    A recurrent back-propagation neural algorithm is trained to classify nine odors. The algorithm is capable of correctly identifying the odors regardless of the time sequence of presentation. The classification is performed in near-real time and is based upon the transient response of an array of 15 tin-oxide gas sensors
  • Keywords
    array signal processing; backpropagation; chemical variables measurement; electric sensing devices; recurrent neural nets; transient response; SnO2; back-propagation; feature-level signal processing; near-real time; odor sensor arrays; odors classification; recurrent neural network algorithm; tin-oxide gas sensors; transient response; Array signal processing; Clustering algorithms; Gas detectors; Neural networks; Petroleum; Sensor arrays; Signal processing algorithms; Software algorithms; Steady-state; Transient response;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Industrial Electronics, Control and Instrumentation, 1997. IECON 97. 23rd International Conference on
  • Conference_Location
    New Orleans, LA
  • Print_ISBN
    0-7803-3932-0
  • Type

    conf

  • DOI
    10.1109/IECON.1997.671050
  • Filename
    671050
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=322910