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Neural Computing and Applications

, Volume 21, Issue 8, pp 1981–1986 | Cite as

Enhancement of the neural network modeling accuracy using a submodeling decomposition-based technique, application in gas sensor

  • Baha Hakim
  • Dibi Zohir
Original Article
  • 239 Downloads

Abstract

This paper presents an algorithm based on the use of artificial neural networks (ANNs) in order to reduce the processing time and to improve the accuracy in ANN modeling, which can be accomplished with a division of the model to submodels by input subintervals. We apply this method with a gas sensor aiming to accurately control the small gas leaks, thus decreasing the risk of false alarms and missed detections. The sensor model accurately, especially in small concentrations, expresses the nonlinear character of the response and the dependence on temperature and relative humidity in addition to the gas nature dependency. The corrector linearizes and compensates the sensor’s responses. The results obtained show the effectiveness of the proposed technique.

Keywords

ANN Subinterval Submodel Gas sensor 

References

  1. 1.
    Viharos ZJ, Kemény Z (2007) AI techniques in modelling, assignment problem solving and optimization. Eng Appl Artif Intell 20:691–698CrossRefGoogle Scholar
  2. 2.
    Kim JY, Kang SW, Shin TZ, Yang MK, Lee KS (2006) Design of a smart gas sensor system for room air-cleaner of automobile-thick-film metal oxide semiconductor gas sensor. IEEE Strateg Technol 20:72–75Google Scholar
  3. 3.
    Gaura E, Newman RM (2004) Smart. Intelligent and cogent microsensors intelligence for sensors and sensors for intelligence, NSTI NanotechnolGoogle Scholar
  4. 4.
    Zhuiykov S (2008) Gas sensor applications of oxygen-ionic electrolytes development of their electron mode. Sens Actuators B Chem 130(1):488–496CrossRefGoogle Scholar
  5. 5.
    Andrei P, Fields LL, Zheng JP, Cheng Y, Xiong P (2007) Modeling and simulation of single nanobelt SnO2 gas sensors with FET structure. Sens Actuators B Chem 128(1):226–234CrossRefGoogle Scholar
  6. 6.
    Fort A, Rocchi S, Santos S, Spinicci R, Vignoli V (2004) Electronic noses based on metal oxide gas sensors the problem of selectivity enhancement. In: Proceedings of the 21st IEEE, instrumentation and measurement, vol 1, pp 599–604Google Scholar
  7. 7.
    Bendahan M, Guérin J, Boulmani R, Aguir K (2007) WO3 sensor response according to operating temperature: experiment and modeling. Sens Actuators B Chem 124(1):24–29CrossRefGoogle Scholar
  8. 8.
    Iglesias GE, Iglesias EA (1988) Linearization of transducer signals using an analog-to-digital converter. IEEE Trans Instrum Meas 37:53–57CrossRefGoogle Scholar
  9. 9.
    Vargha B, Zoltán I (2001) Calibration algorithm for current-output R-2R ladders. IEEE Trans Instrum Meas 50:1216–1220CrossRefGoogle Scholar
  10. 10.
    Renneberg C, Lehmann T (2007) Analog circuits for thermistor linearization with Chebyshev-optimal linearity error. (ECCTD07), August 26–30. Sevilla, SpainGoogle Scholar
  11. 11.
    Cristaldi L, Ferro A, Lazzaroni M, Ottoboni R (2001) A linearization method for commercial Hall-effect current transducer. IEEE Trans Instrum Meas 50(5):1149–1153CrossRefGoogle Scholar
  12. 12.
    James HT, Antoniotti AJ (1993) Linearisation algorithms for computer-aided control engineering. IEEE Contr Syst Magaz 13(2):58–64CrossRefGoogle Scholar
  13. 13.
    Patranbis D, Gosh D (1989) A novel software based transducer linearizer. IEEE Trans Instrum Meas 38(6):1122–1126CrossRefGoogle Scholar
  14. 14.
    Malcovati P, Leme CA, O’Leary P, Maloberti F, Baltes H (1994) Smart sensor interface with A/D conversion and programmable calibration. IEEE J Solid-State Circuits 29:963–966CrossRefGoogle Scholar
  15. 15.
    Baha H, Dibi Z (2009) Aspects of gas sensor’s modeling and implementation in a dynamic environment. Sens Trasducer J 109(10):1–12Google Scholar
  16. 16.
    Baha H, Dibi Z (2009) A novel neural network-based technique for smart gas sensors operating in a dynamic environment. Sensors 9:8944–8960CrossRefGoogle Scholar
  17. 17.
    Viharos ZJ, Monostori L, Novák K, Tóth G, Csongrádi Z, Kenderesy T, Sólymosi T, Lőrincz A, Kóródi T (2003) Monitoring of complex production systems in view of digital factorie. In: Proceedings of the 17th IMEKO World Congress Metrology in the 3rd Millennium. pp 1463–1468Google Scholar
  18. 18.
    Viharos ZJ (2005) Automatic generation of a net of models for high and low levels of production. In: Proceedings of the 16th IFAC World Congress, Reg. No. 05127Google Scholar
  19. 19.
    Figaro Gas Sensor Company (2000) Technical information on usage of TGS sensors for toxic and explosive gas leak detectors: Figaro Gas Sensor Company: Osaka, JapanGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.Electronics’ Department University of BatnaBatnaAlgeria
  2. 2.Laboratory LEABatnaAlgeria

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