Skip to main content
Log in

A virtual instrumentation approach to neural network-based thermistor linearization on field programmable gate array

  • Technical Article
  • Published:
Experimental Techniques Aims and scope Submit manuscript

Abstract

Temperature measurement is an important industrial requirement in several applications. Thermistor, in particular, is used to a great extent for this purpose in many industrial applications as it is cost effective, relatively small in size, and has better sensitivity as compared to its counterparts. It offers a moderate range of temperature sensing typically from −55°C to 125°C. On the other hand, thermistor is a highly nonlinear sensor as it is characterized by the exponential dependency of resistance on temperature. Effective usage of thermistor thus requires some mechanism for linearization. This paper presents a simple step-by-step, practically implementable artificial neural network (ANN)-based linearization method for thermistor characteristic using a two-layer neural network having two neurons in each layer. The trained feed-forward neural network is implemented on a field programmable gate array (FPGA) on the NI-PXI platform for real-time measurement. Validation of the proposed technique was carried out experimentally using a comparative study. A precise thermocouple-based temperature measurement system was utilized for this purpose. The temperature readings were recorded after allowing both the sensors to settle, and a maximum error of ±0.9°C was obtained in the experimental measurement range of 5°C–65°C.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Bosson, G., Guttmann, F., and Simmons, L.M., “Relationship between Temperature and Resistance of a Thermistor,” Journal of Applied Physics 21(12): 1267–1268 (1950).

    Article  Google Scholar 

  2. Diamond, J.M., “Linearization of Resistance Thermometers and Other Transducers,” Review of Scientific Instruments 41(1): 53–60 (1970).

    Article  Google Scholar 

  3. Khan, A.A., Al Turaigi, M.A., and Alamoud, A.R.M., “Linearized Thermistor using an Analog Multiplier,” IEEE Transactions on Instrumentation and Measurement 37(2): 322–323 (1988).

    Article  Google Scholar 

  4. Natarajan, S., “A Modified Linearized Thermistor Thermometer using an Analog Multiplier,” IEEE Transactions on Instrumentation and Measurement 39(2): 440–441 (1990).

    Article  Google Scholar 

  5. Nenova, Z.P., and Nenov, T.G., “Linearization Circuit of the Thermistor Connection,” IEEE Transactions on Instrumentation and Measurement 58(2): 441–449 (2009).

    Article  Google Scholar 

  6. Tsai C. F., Li L. T., Li C.-H., and Young M. S., “Implementation of Thermistor Linearization using LabVIEW,” Proceedings of the IEEE Computer Society Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, Kyoto, Japan, September 12–14 (2009).

  7. Hagan, M.T., Dcmuth, H.B., and Beale, M., Neural Network Design, Thomson Asia Pvt. Ltd., Singapore (2003).

    Google Scholar 

  8. Wing Lian Ming, Deng Yu Fen, Liu Bao Liang, and Zhao Xian Long. “A Neural Network Approach for Creating a NTC Thermistor Model Library for PSPICE,” Proceedings of the IEEE Conference on Cybernetics and Intelligent Systems, Chengdu, China, September 21–24 (2008).

  9. Attari M., Boudjema F., and Heniche M., “Linearizing a Thermistor Characteristics in the Range 0-100°C with Two Layer ANN,” Proceedings of the IEEE Instrumentation and Measurement Technology Conference on Integrating Intelligent Instrumentation and Control, Waltham, MA, April 24–26 (1995).

  10. Mahana, P.N., and Trofimenkoff, F.N., “Transducers Output Signal Processor using an 8-Bit Microcomputer,” IEEE Transactions on Instrumentation and Measurement 35(2): 182–186 (1986).

    Article  Google Scholar 

  11. Ghosh, D., and Patranabis, D., Software Based “Linearization of Thermistor Type Nonlinearity,” Proceedings of the IEE G Circuits, Devices & Systems 139(3): 339–342 (1992).

    Article  Google Scholar 

  12. Mohan, N.M., Kumar, V.J., and Sankaran, P., “Linearizing Dual Slope Digital Converter Suitable for a Thermistor,” IEEE Transactions on Instrumentation and Measurement 60(5): 1515–1521 (2011).

    Article  Google Scholar 

  13. Sonowal D., and Bhuyan M., “FPGA Implementation of Neural Networks for Linearization of Thermistor Characteristics,” Proceedings of IEEE International Conference on Devices, Circuits and Systems, Coimbatore, India, March 15–16 (2012).

  14. M/s Microchip Application Notes, Precision Temperature-Sensing with RTD Circuits, URL http://ww1.microchip.com/downloads/en/appnotes/00687c.pdf [accessed on 24 February 2012].

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to K. P. S. Rana.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rana, K.P.S., Mittra, N., Pramanik, N. et al. A virtual instrumentation approach to neural network-based thermistor linearization on field programmable gate array. Exp Tech 39, 23–30 (2015). https://doi.org/10.1111/ext.12011

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1111/ext.12011

Keywords

Navigation