IJE TRANSACTIONS A: Basics Vol. 31, No. 10 (October 2018) 1682-1688   

downloaded Downloaded: 66   viewed Viewed: 566

J. Ghasemi, M. Mehdipoor, J. Rasekhi and K. Gorgani Firouzjah
( Received: June 01, 2018 – Accepted in Revised Form: August 17, 2018 )

Abstract    Thermistors are very commonly used for narrow temperature-range high-resolution applications, such as in medicine, calorimetry, and near ambient temperature measurements. In particular, Negative Temperature Coefficient (NTC) thermistor is very inexpensive and highly sensitive, whose sensing temperature range and sensitivity are highly limited due to the intrinsic nonlinearity and self-heating properties of NTC thermistor at high operation currents. In this research, a new structure is proposed based on adaptive neuro-fuzzy system for the modeling of sensor nonlinearity. Apart from taking self-heating phenomenon of NTC thermistor sensor, the proposed structure also measures temperature directly, without any linearizing circuitry. Neuro-fuzzy network is trained and tested through data produced in the Laboratory environment. Examination of the proposed method on test data achieved a mean squared error of 0.0195, which is considered as a significant accomplishment.


Keywords    Temperature, Negative Temperature Coefficient Thermistor, Self-heating, Modeling, Adaptive Neuro-fuzzy Inference System



ترميستور با مقاومت منفي (NTC) از جمله سنسورهاي دمايي است که با داشتن قيمت بسيار پايين و حساسيت به مراتب بالا، به دليل غيرخطي بودن و پديده خودگرمايي کمتر از آن استفاده مي‌شود. در اين پژوهش، ساختاري جديد مبتني بر شبکه عصبي-فازي تطبيقي براي مدلسازي غيرخطي بودن سنسور پيشنهاد شده است. ساختار پيشنهادي علاوه بر در نظر گرفتن پديده خودگرمايي سنسور NTC، دما را بدون استفاده از مدارات خطي‌ساز و يا مدارات واسط ديگر، بطور مستقيم اندازه‌گيري مي‌کند. در اين پژوهش، با استفاده از داده‌هايي که در آزمايشگاه توليد شده‌اند، شبکه عصبي-فازي تطبيقي آموزش و آزمايش شده است. بررسي نتايج روش پيشنهادي بر روي داده‌هاي آزمايش، ميانگين مربعات خطاي 0/0195 را نشان مي‌دهد که دستاورد قابل ملاحظه اي است.


1. Cotton, N.J., and Wilamowski, B.M., “Compensation of Sensors Nonlinearity with Neural Networks”, In 24th IEEE International Conference on Advanced Information Networking and Applications, IEEE, (2010), 1210–1217.
2. Samuel Rajesh Babu M.E., R., Deepa M.E., S., and Jothivel M.E., S., “A Closed Loop Control of Quadratic Boost Converter Using PID Controller”, International Journal of Engineering - Transactions B: Applications, Vol. 27, No. 11, (2014), 1653–1662.
3. Mahmoudzadeh, S., Mojallali, H., and Pourjafari, N., “An Optimized PID for Capsubots using Modified Chaotic Genetic Algorithm (RESEARCH NOTE)”, International Journal of Engineering - Transactions C: Aspects, Vol. 27, No. 9, (2014), 1377–1384.
4. Takagi, T., and Sugeno, M., “Fuzzy identification of systems and its applications to modeling and control”, IEEE Transactions on Systems, Man, and Cybernetics, Vol. SMC-15, No. 1, (1985), 116–132.
5. Moghadam-Fard, H., and Samadi, F., “Active Suspension System Control Using Adaptive Neuro Fuzzy (ANFIS) Controller”, International Journal of Engineering - Transactions C: Aspects,  Vol. 28, No. 3, (2014), 396–401.
6. Bahramifar, A., Shirkhani, R., and Mohammadi, M., “An ANFIS-based Approach for Predicting the Manning Roughness Coefficient in Alluvial Channels at the Bank-full Stage”, International Journal of Engineering - Transactions B: Applications,  Vol. 26, No. 2, (2012), 177–186.
7. Khan, A.A., and Sengupta, R., “A Linear Temperature/Voltage Converter Using Thermistor in Logarithmic Network”, IEEE Transactions on Instrumentation and Measurement, Vol. 33, No. 1, (1984), 2–4.
8. Childs, P.R.N., Practical temperature measurement, Butterworth-Heinemann, Elsevier, (2001).
9. Bandyopadhyay, S., Das, A., Mukherjee, A., Dey, D., Bhattacharyya, B., and Munshi, S., “A Linearization Scheme for Thermistor-Based Sensing in Biomedical Studies”, IEEE Sensors Journal, Vol. 16, No. 3, (2016), 603–609.
10. Sarkar, A.R., Dey, D., and Munshi, S., “Linearization of NTC Thermistor Characteristic Using Op-Amp Based Inverting Amplifier”, IEEE Sensors Journal,  Vol. 13, No. 12, (2013), 4621–4626.
11. Abdulwahab, D., Khan, S., Chebil, J., Ahmed, M.M., Naji, A.W.A.K., and Alam, A.H.M.Z., “Identification of linearized regions of non-linear transducers responses”, In International Conference on Computer and Communication Engineering (ICCCE’10), IEEE, (2010), 1–4.
12. Rana, K.P.S., Mittra, N., Pramanik, N., Dwivedi, P., and Mahajan, P., “A Virtual Instrumentation Approach to Neural Network-Based Thermistor Linearization on Field Programmable Gate Array”, Experimental Techniques, Vol. 39, No. 2, (2015), 23–30.
13. Lim, E.A., and Jayakumar, Y., “A study of neuro-fuzzy system in approximation-based problems”, Matematika, Vol. 24, (2008), 113–130.
14. Cotton, N.J., Wilamowski, B.M., and Dundar, G., “A Neural Network Implementation on an Inexpensive Eight Bit Microcontroller”, In International Conference on Intelligent Engineering Systems, IEEE, (2008), 109–114.
15. Echanobe, J., del Campo, I., Basterretxea, K., Martinez, M.V., and Doctor, F., “An FPGA-based multiprocessor-architecture for intelligent environments”, Microprocessors and Microsystems, Vol. 38, No. 7, (2014), 730–740.
16. Goh, Y.L., Ramasamy, A.K., Nagi, F.H., and Zainul Abidin, A.A., “DSP based fuzzy and conventional overcurrent relay controller comparisons”, Microelectronics Reliability, Vol. 53, No. 7, (2013), 1029–1035.
17. Jovic, S., Anicic, O., and Pejovic, B., “Management of the wind speed data using adaptive neuro-fuzzy methodology”, Flow Measurement and Instrumentation, Vol. 50, (2016), 201–208.
18. Bouhedda, M., “Neuro-fuzzy sensor’s linearization based FPGA”, In IEEE 7th International Conference on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS), IEEE, (2013), 324–328.
19. Chong, W.T., Gwani, M., Shamshirband, S., Muzammil, W.K., Tan, C.J., Fazlizan, A., Poh, S.C., Petković, D., and Wong, K.H., “Application of adaptive neuro-fuzzy methodology for performance investigation of a power-augmented vertical axis wind turbine”, Energy, Vol. 102, (2016), 630–636.
20. Chong, W.T., Al-Mamoon, A., Poh, S.C., Saw, L.H., Shamshirband, S., and Mojumder, J.C., “Sensitivity analysis of heat transfer rate for smart roof design by adaptive neuro-fuzzy technique”, Energy and Buildings, Vol. 124, (2016), 112–119.
21. Depari, A., Flammini, A., Marioli, D., and Taroni, A., “Application of an ANFIS Algorithm to Sensor Data Processing”, IEEE Transactions on Instrumentation and Measurement, Vol. 56, No. 1, (2007), 75–79.
22. Steinhart, J.S., and Hart, S., “Calibration curves for thermistors”, Deep Sea Research and Oceanographic Abstracts, Vol. 15, No. 4, (1968), 497–503.
23. White, D.R., “Interpolation Errors in Thermistor Calibration Equations”, International Journal of Thermophysics, Vol. 38, No. 4, (2017), 1–11.
24. Jang, J.S.R., “Fuzzy Modeling Using Generalized Neural Networks and Kalman Filter Algorithm”, AAAI, Vol. 91, (1991), 762–767.
25. Jang, J.S.R., “ANFIS: adaptive-network-based fuzzy inference system”, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 23, No. 3, (1993), 665–685.
26. Sivanandam, S., Sumathi, S., and Deepa, S., Introduction to fuzzy logic using MATLAB, Vol. 1, Berlin: Springer, (2007).

International Journal of Engineering
E-mail: office@ije.ir
Web Site: http://www.ije.ir