The accuracy of weighing systems is easily affected by sensors, conversion circuits, temperature, and other factors. Aiming at the problem of significant temperature impact during the operation of the weighing system, which leads to a decrease in weighing accuracy. This article proposes a BP neural network method for temperature compensation of the weighing system to further improve the weighing accuracy. Firstly, a weighing system is built based on STM32 to collect system temperature and weighing data. Then, a BP neural network is built using MATLAB, and the calibration data is imported to train the optimal model of the BP neural network. Finally, the model parameters are imported into an embedded system to achieve real-time temperature compensation for the STM32 weighing system. The experimental results show that the accuracy and stability of the weighing system have been improved, and it has good application value.
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