Development of thermistor signal conditioning circuit using artificial neural networks

Development of thermistor signal conditioning circuit using artificial neural networks

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Thermistor is most widely used sensor in the temperature measurement due to its high sensitivity and fast response. The non-linearity of the thermistor gives rise to several difficulties for on-chip interface, direct digital readout, wireless transmission and so on. Hence, an effective lineariser is needed to overcome the difficulties. In this study, an artificial neural network-based lineariser has been developed for the thermistor connected in operational amplifier circuit. Operational amplifier-based thermistor signal conditioning circuit exhibits a stable temperature–voltage relation over a range of 0–100°C with low linearity. A multilayer perceptron feed-forward neural network is used for non-linearity compensation of thermistor circuit to further improve the linearity. A linearity of ±0.3% is achieved over 0–100°C with high temperature stability. A notable feature of the proposed method is the non-linearity error remains low over the entire dynamic range of the thermistor. The efficacy of the method is established through simulation studies and its practicality demonstrated with experimental results obtained on a prototype unit built and tested.


    1. 1)
      • 1. Ramon, P.A., Webster, J.G.: ‘Sensors and signal conditioning’ (John-Wiley, 2013).
    2. 2)
    3. 3)
    4. 4)
    5. 5)
    6. 6)
    7. 7)
      • 7. Rosa, V.C., Palma, L.S., Oliveira, A., Torres, T.R.: ‘An inherently linear transducer using thermistor practical approach’. Proc. of third Int. Conf. on Sensor Technologies, 2008, pp. 491495.
    8. 8)
    9. 9)
    10. 10)
    11. 11)
    12. 12)
    13. 13)
    14. 14)
    15. 15)
    16. 16)
      • 16. Patra, J.C., Pal, R.N.: ‘Inverse modeling of pressure sensors using artificial neural networks’. Proc. of AMSE Int. Conf. on Signals, Data, and Systems, Bangalore, India, 1993, pp. 225236.
    17. 17)
      • 17. Khan, S.K., Agarwala, A.K., Shahani, D.T.: ‘Artificial neural network (ANN) based nonlinearity estimation of thermistor temperature sensors’. Proc. of 24th National Systems Conf., Bangalore, India, 2000, pp. 296302.
    18. 18)
    19. 19)
    20. 20)
      • 20. Attari, M., Boudjema, F., Heniche, M.: ‘Linearizing a thermistor characteristic in the range of zeroto 100 degree Cwith two layers artificial neural networks’. Proc. of Int. Conf. on Instrumentation and Measurement Technology, 1995, pp. 119122.
    21. 21)
    22. 22)
      • 22. Volgin, L.I.: ‘Electrical transducers for measurement instruments and systems’ (Sovetskoe Radio, Moscow, Russia, 1971) (in Russian).
    23. 23)
      • 23. Hagan, M.T., Demuth, H.B.: ‘Neural network design’ (China Machine Press, Suzhou, China, 2005).
    24. 24)
    25. 25)
      • 25. Haykin, S.: ‘Neural networks: a comprehensive foundation’ (Prentice-Hall, 2008).
    26. 26)
    27. 27)
      • 27. Hagan, M.T., Demuth, H.B., Beale, M.: ‘Neural network design’ (PWS-Kent, Boston, MA, USA, 1996).
    28. 28)
    29. 29)

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