Abstract
High precision and smart sensors make up an indispensable data entry for the Internet of Things technology. Nonetheless, conventional calibration algorithms mainly implemented on the software, such as least squares, polynomial fitting, and interpolation, exhibit limited calibration accuracy that does not reflect a real-time measurement of the sensors. The problem can be resolved with an MCU-based sensor calibration system proposed herein, which mainly employs particle swarm optimization (PSO)-back propagation (BP) neural network. The system firstly reads sensor data through I2C bus and then uses the BP neural network and PSO algorithm to automatically calibrate these data in real time. Sigmoid activation function was implemented via a piecewise polynomial fitting to create a trade-off between hardware resource and precision. A performance test conducted on temperature sensors showed a maximum error of 0.16 °C within the measurement range of −40–100 °C with three times the standard deviation (3σ) error of ±0.23 °C and overall linearity of 0.1143% after the calibration system was added as compared to the significantly higher error of ±0.63 °C without the calibration.
Published Version
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