Abstract
This paper presents a calibration technique for a Resistance Temperature Detector (RTD) used in measurement of temperature. Soft calibration circuit is designed using an optimized Artificial Neural Network (ANN), optimization of ANN is to choose a particular neural network scheme, algorithm, transfer function, and number of hidden layers. The objective of the present work is design an adaptive calibration circuit using optimized ANN model which produces (i) a linear output over 100% of full scale input range, (ii) accurate output even when the RTD is replaced with a different RTD (different refers to variation in parameter like reference resistance and temperature coefficient). Resistance temperature detector is cascaded to the designed neural network model through a suitable data conversion circuit. The designed system is evaluated for its performance in simulation and practical domain. Results obtained show that the set objectives are fulfilled.
| Original language | English |
|---|---|
| Pages (from-to) | 153-161 |
| Number of pages | 9 |
| Journal | Engineering Intelligent Systems |
| Volume | 23 |
| Issue number | 3 |
| Publication status | Published - 01-09-2015 |
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All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Computer Science Applications
- Electrical and Electronic Engineering
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A practically validated adaptive calibration technique for temperature measurement using resistance temperature detector. / Santhosh, K. V.; Roy, B. K.
In: Engineering Intelligent Systems, Vol. 23, No. 3, 01.09.2015, p. 153-161.Research output: Contribution to journal › Article
TY - JOUR
T1 - A practically validated adaptive calibration technique for temperature measurement using resistance temperature detector
AU - Santhosh, K. V.
AU - Roy, B. K.
PY - 2015/9/1
Y1 - 2015/9/1
N2 - This paper presents a calibration technique for a Resistance Temperature Detector (RTD) used in measurement of temperature. Soft calibration circuit is designed using an optimized Artificial Neural Network (ANN), optimization of ANN is to choose a particular neural network scheme, algorithm, transfer function, and number of hidden layers. The objective of the present work is design an adaptive calibration circuit using optimized ANN model which produces (i) a linear output over 100% of full scale input range, (ii) accurate output even when the RTD is replaced with a different RTD (different refers to variation in parameter like reference resistance and temperature coefficient). Resistance temperature detector is cascaded to the designed neural network model through a suitable data conversion circuit. The designed system is evaluated for its performance in simulation and practical domain. Results obtained show that the set objectives are fulfilled.
AB - This paper presents a calibration technique for a Resistance Temperature Detector (RTD) used in measurement of temperature. Soft calibration circuit is designed using an optimized Artificial Neural Network (ANN), optimization of ANN is to choose a particular neural network scheme, algorithm, transfer function, and number of hidden layers. The objective of the present work is design an adaptive calibration circuit using optimized ANN model which produces (i) a linear output over 100% of full scale input range, (ii) accurate output even when the RTD is replaced with a different RTD (different refers to variation in parameter like reference resistance and temperature coefficient). Resistance temperature detector is cascaded to the designed neural network model through a suitable data conversion circuit. The designed system is evaluated for its performance in simulation and practical domain. Results obtained show that the set objectives are fulfilled.
UR - http://www.scopus.com/inward/record.url?scp=85014733981&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85014733981&partnerID=8YFLogxK
M3 - Article
VL - 23
SP - 153
EP - 161
JO - International Journal of Engineering Intelligent Systems for Electrical Engineering and Communications
JF - International Journal of Engineering Intelligent Systems for Electrical Engineering and Communications
SN - 0969-1170
IS - 3
ER -