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基于机器学习的NTC温度校准方法研究

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基于机器学习的NTC热敏电阻温度校准方法由于制造过程和存储环境因素的差异,会导致传统校准方法可能存在一定的测量误差.本文采用机器学习技术中的支持向量机回归模型,对4组NTC热敏电阻样本进行温度校准.结果表明:该模型相对于传统方法,预测热敏电阻温度值与标准温度值的偏差为(-0.02~0.02)℃,具有更高的精确度和准确性.
Research on NTC temperature Calibration Method Based on Machine Mearning
The NTC thermistor temperature calibration method based on machine learning may have some measure-ment errors due to the differences in manufacturing process and storage environment.In this paper,the support vec-tor machine regression model in machine learning technology is used to calibrate 4 groups of NTC thermistor sam-ples.The results show that compared with the traditional method,the deviation between the temperature of ther-mistor and the standard temperature is(-0.02~0.02)℃,and the model has higher precision and accuracy.

NTC thermistortemperature calibrationmachine learningpolynomial fitting

张译、张进成、孙志平

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云南省普洱市质量技术监督综合检测中心

云南省大理州质量技术监督综合检测中心

NTC热敏电阻 温度校准 机器学习 多项式拟合

2024

计量与测试技术
成都市计量监督检定测试所

计量与测试技术

影响因子:0.175
ISSN:1004-6941
年,卷(期):2024.51(8)