Advertisement

ANN modeling of a smart MEMS-based capacitive humidity sensor

  • Kouda SouhilEmail author
  • Dibi Zohir
  • Barra Samir
  • Dendouga Abdelghani
  • Meddour Fayçal
Technical Notes and Correspondence

Abstract

This paper presents a design of a smart humidity sensor. First we begin by the modeling of a Capacitive MEMS-based humidity sensor. Using neuronal networks and Matlab environment to accurately express the non-linearity, the hysteresis effect and the cross sensitivity of the output humidity sensor used. We have done the training to create an analytical model CHS “Capacitive Humidity Sensor”. Because our sensor is a capacitive type, the obtained model on PSPICE reflects the humidity variation by a capacity variation, which is a passive magnitude; it requires a conversion to an active magnitude, why we realize a conversion capacity/voltage using a switched capacitor circuit SCC. In a second step a linearization, by Matlab program, is applied to CHS response whose goal is to create a database for an element of correction “CORRECTOR”. After that we use the bias matrix and the weights matrix obtained by training to establish the CHS model and the CORRECTOR model on PSPICE simulator, where the output of the first is identical to the output of the CHS and the last correct its nonlinear response, and eliminate its hysteresis effect and cross sensitivity. The three blocks; CHS model, CORRECTOR model and the capacity/voltage converter, represent the smart sensor.

Keywords

CORRECTOR humidity sensor MEMS MLP neuronal network smart sensor 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    T. Islam, S. Ghosh, and H. Saha, “ANN-based signal conditioning and its hardware implementation of a nanostructured porous silicon relative humidity sensor,” Sensors and Actuators B, vol. 120, no. 1, pp. 130–141, March 2006.CrossRefGoogle Scholar
  2. [2]
    J. C. Patra and G. Panda, “ANN-based intelligent pressure sensor in noisy environment,” Measurement, vol. 23, no. 4, pp. 229–238, December 1998.CrossRefGoogle Scholar
  3. [3]
    S. Kouda, Z. Dibi, A. Dendouga, and S. Barra, “Optimization of a Novel Humidity Sensing Mechanism Strip Length,” Sensors & Transducers, vol. 104, no. 5, pp. 96–108, May 2009.Google Scholar
  4. [4]
    J. C. Patra and A. V. Bos, “Modeling and development of an ANN-based smart pressure sensor in a dynamic environment,” Measurement, vol. 26, no. 4, pp. 249–262, July 1999.CrossRefGoogle Scholar
  5. [5]
    P. Singh, T. S. Kamal, and S. Kumar, “Development of ANN-based virtual fault detector for Wheatstone bridge-oriented transducers,” IEEE Sensors Journal, vol. 5, no. 5, pp. 1043–1049, October 2005.CrossRefGoogle Scholar
  6. [6]
    P. Arpaia, P. Daponte, D. Grimaldi, and L. Michaeli “ANN-based error reduction for experimentally modeled sensors,” IEEE Trans. on Instrumentation and Measurement, vol. 51, no. 1, pp. 23–30, February 2002.CrossRefGoogle Scholar
  7. [7]
    S. K. Mandal, S. Sural, and A. Patra, “ANN-and PSO-based synthesis of on-chip spiral inductors for RF ICs,” IEEE Trans. on Computer-aided Design of Integrated Circuits and Systems, vol. 27, no. 1, pp. 188–192, January 2008.CrossRefGoogle Scholar
  8. [8]
    S. Kouda, Z. Dibi, and F. Meddour, “Modeling of thermal-conductivity of smart humidity sensor,” Proc. of the 2nd Conf. Signals, Circuits and Systems, pp. 1–4, 2008.Google Scholar
  9. [9]
    S. Kouda, Z. Dibi, and F. Meddour, “ANN modeling of resistive humidity sensor,” Proc. of the 2nd International Conference on Electrical Engineering Design and Technologies, pp. 1–4, 2008.Google Scholar
  10. [10]
    C.-Y Lee and G.-B. Lee, “MEMS-based humidity sensors with integrated temperature sensors for signal drift compensation,” IEEE Sensors, vol. 1, no. 1, pp. 384–388, October 2003.Google Scholar
  11. [11]
    M. L. Hafiane, Z. Dibi, L. Saidi, and A. Hafiane, “Modeling of a capacitive pressure sensor using artificial neural networks,” Proc. of the 2nd Conf. Information and Communication Technologies, pp. 204–209, 2006.Google Scholar
  12. [12]
    R. Pallaás-Areny and J. G. Webster, Sensors And Signal Conditioning, Wiley-Interscience Publication, USA, 2001.Google Scholar
  13. [13]
    M. Michalik, M. Łukowicz, W. Rebizant, S.-J. Lee, and S.-H. Kang, “New ANN-based algorithms for detecting HIFs in multigrounded MV networks,” IEEE Trans. on Power Delivery, vol. 23, no. 1, pp. 58–66, January 2008.CrossRefGoogle Scholar
  14. [14]
    M. Bishop, Neural Networks for Pattern Recognition, OXFORD, UK, 1995.Google Scholar
  15. [15]
    J. C. Patra, E. L. Ang, N. S. Chaudhari, and A. Das, “Neural-network-based smart sensor framework operating in a harsh environment,” Hindawi Publishing Corporation, vol. 2005, no. 4, pp. 558–574, July 2005.zbMATHGoogle Scholar
  16. [16]
    M. Yamada and K. Watanabe “A capacitive pressure sensor interface using oversampling Δ-Σ demodulation techniques,” IEEE Trans. on Instrumentation and Measurement, vol. 46, no. 1, pp 3–7, February 1997.CrossRefGoogle Scholar
  17. [17]
    J. C. Patra, A. C. Kot, and G. Panda, “An intelligent pressure sensor using neural networks,” IEEE Trans. on Instrumentation and Measurement, vol. 49, no. 4, pp. 829–834, August 2000.CrossRefGoogle Scholar

Copyright information

© Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Kouda Souhil
    • 1
    Email author
  • Dibi Zohir
    • 1
  • Barra Samir
    • 1
  • Dendouga Abdelghani
    • 1
  • Meddour Fayçal
    • 1
  1. 1.LEA, département d’électroniqueUniversité de BatnaBatnaAlgérie

Personalised recommendations