Implementation of Artificial Neural Network-Based Signal Conditioning Circuit for a Temperature Transducer

Thermistors are widely used as temperature transducers due to their low cost, small dimensions, and high sensitivity. However, their nonlinear resistance-temperature characteristics make their use in temperature measurement and control applications challenging. This paper presents a new method to linearize thermistors using an op-amp-based astable multivibrator and an artificial neural network. The proposed technique involves obtaining frequency values corresponding to the resistance values of the thermistor using an op-amp-based astable multivibrator circuit. The nonlinear frequency data obtained can then be used to train an ANN to obtain the best-fit linear curve. A signal conditioning circuit is then constructed that takes the read frequency value and matches it to its corresponding linearized value, which is displayed. The method offers several advantages over traditional techniques, including improved accuracy and reduced cost. Additionally, it can improve the accuracy of measuring temperature, and control applications by eliminating the nonlinearities associated with thermistors.