Fuzzy Logic and Neural Network Applications on the Gas Sensor Data: Concentration Estimation

  • Fevzullah Temurtas
  • Cihat Tasaltin
  • Hasan Temurtas
  • Nejat Yumusak
  • Zafer Ziya Ozturk
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2869)


In this study, a fuzzy logic based algorithm is presented for the concentration estimation of the CCl4 and CHCl3 gases by using the steady state sensor response and an artificial neural network (ANN) structure is proposed for the concentration estimation of the same gases inside the sensor response time by using the transient sensor response. The Quartz Crystal Microbalance (QCM) type sensors were used as gas sensors. A computer controlled measurement and automation system with IEEE 488 card was used to control the gas concentration values and to collect the sensor responses. Acceptable performance was obtained for the concentration estimation with fuzzy inference. The appropriateness of the artificial neural network for the gas concentration determination inside the sensor response time is observed.


Artificial Neural Network Membership Function Fuzzy Logic Frequency Shift Sensor Response 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ho, M.H., Gullbault, G.G., Rietz, B.: Continuos Detection of Toluene in Ambient Air with a Coated Piezoelectric Crystal. Anal. Chem. 52(9) (1980)Google Scholar
  2. 2.
    Vaihinger, S., Gopel, W.: Multi - Component Analysis in Chemical Sensing in Sensors: A Comprehensive Survery. In: Gopel, W., Hense, S., Zemel, S.N. (eds.), vol. 2(1), p. 192. VCH. Weinhe, New York (1991)Google Scholar
  3. 3.
    Ozturk, Z.Z., Zhou, R., Wiemar, U., Ahsen, V., Bekaroglu, O., Gopel, W.: Soluble Phthalocyanines For the Detection of Organic Solvents Thin Film Structure With Quartz Microbalance and Capacitance Transducers. Sensors And Actuators B 26–27, 208–212 (1995)CrossRefGoogle Scholar
  4. 4.
    Zhou, R., Josse, F., Gopel, W., Ozturk, Z.Z., Bekaroglu, A.: Phthalocyanines As Sensitive Materyals For Chemical Sensors. Applied Organometallic Chemistry 10, 557–577 (1996)CrossRefGoogle Scholar
  5. 5.
    Gopel, W., Schierbaum, K.D.: Sensors A Comprehensive Survey, vol. ch. 1, pp. 18–27. VCH Weinheim, New York (1991)Google Scholar
  6. 6.
    King, H.W.: Piezoelectric Sorption Detector. Anal. Chem. 36, 1735–1739 (1964)CrossRefGoogle Scholar
  7. 7.
    Riddick, J., Bunger, A., Weissberger, A.: Organic Solvents in Techniques of Chemistry, vol. 2. Wiley Interscience, Hoboken (1970)Google Scholar
  8. 8.
    Yea, B., Osaki, T., Sugahara, K., Konishi, R.: The concentration estimation of inflammable gases with a semiconductor gas sensor utilizing neural networks and fuzzy inference. Sensors and Actuators-B 41, 121–129 (1997)CrossRefGoogle Scholar
  9. 9.
    Mamdani, E.H., Assilian, S.: An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies 7(1), 1–13 (1975)zbMATHCrossRefGoogle Scholar
  10. 10.
    Haykin, S.: Neural Networks. In: A Comprehensive Foundation. Macmillan Publishing Company, Englewood Cliffs (1994)Google Scholar
  11. 11.
    Huyberechts, G., Szecowka, P., Roggen, J., Licznerski, B.W.: Sensors & Actuators B 41, 123–130 (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Fevzullah Temurtas
    • 1
  • Cihat Tasaltin
    • 2
  • Hasan Temurtas
    • 3
  • Nejat Yumusak
    • 1
  • Zafer Ziya Ozturk
    • 2
  1. 1.Department of Computer EngineeringSakarya UniversityAdapazariTurkey
  2. 2.Tubitak Marmara Research CenterMaterial and Chem. Tec. Res. InstGebzeTurkey
  3. 3.Department of Electric, Electronic EngineeringDumlupinar UniversityKutahyaTurkey

Personalised recommendations