An adaptive calibration circuit for RTD using optimized ANN

Design of an adaptive calibration circuit for temperature measurement using RTD with an optimized Artificial Neural Network (ANN) is reported in this paper. The objectives of the present work are: (i) to extend the linearity range of measurement to 100% of full scale input range, (ii) to make the measurement technique adaptive to variations in reference resistance, and temperature coefficient, and (iii) to achieve objectives (i) and (ii) using an optimized neural network. Optimized neural network model is designed with various algorithms, and transfer function of neuron considering a particular scheme. The output of RTD is resistance. It is converted to voltage by using a suitable data conversion unit. A suitable optimal ANN is added in place of conventional calibration circuit. ANN is trained with simulated data considering variations in reference resistance and temperature coefficient to achieve desired objectives from proposed technique. Results show that the proposed technique has fulfilled the objectives.