• DocumentCode
    505336
  • Title

    Neural network for the modeling of NOx microsensor with sensing element cardo polysulfone

  • Author

    Telipan, G. ; Curteanu, S. ; Cozan, V.

  • Author_Institution
    Nat. R&D Inst. for Electr. Eng. (ICPE-CA), Bucharest, Romania
  • Volume
    1
  • fYear
    2009
  • fDate
    12-14 Oct. 2009
  • Firstpage
    293
  • Lastpage
    296
  • Abstract
    The sensor was made by thick film technology. A polymeric structure as sensitive layer containing cardo polysulfone has been tested in nitric oxides sensing. Feedforward neural networks with two hidden layers are used in mathematical modeling of the system, to predict the voltage of the sensor at a certain time. In this way, the efficiency of the sensor can be appreciated. An alternative methodology of modeling was based on empirical equations that explicitly render the characteristic voltage - time. Both types of models provide accurate predictions that means they describe well the actual behavior of the sensor.
  • Keywords
    electrical engineering computing; feedforward neural nets; gas sensors; microsensors; cardo polysulfone; feedforward neural networks; gas sensor; microsensor; nitric oxides sensing; system mathematical modeling; thick film technology; Feedforward neural networks; Mathematical model; Neural networks; Polymers; Sensor phenomena and characterization; Sensor systems; Testing; Thick film sensors; Thick films; Voltage; gas sensor; mathematical modeling; neural network; polysulfone; thick technology;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Semiconductor Conference, 2009. CAS 2009. International
  • Conference_Location
    Sinaia
  • ISSN
    1545-827X
  • Print_ISBN
    978-1-4244-4413-7
  • Type

    conf

  • DOI
    10.1109/SMICND.2009.5336543
  • Filename
    5336543