Research on temperature calibration method based on random forest algorithm
Based on the fact that thermistor NTC can cause lower measurement precision and lower accuracy performance under the influence of objective factors such as production process and storage conditions,and that the traditional calibration method is affected by the nonlinear temperature characteristics of thermistor,which can not satisfy the calibration results of high precision and accuracy,this paper proposes to introduce the random forest algorithm model in machine learning,and in the experimental conditions of the temperature range of 0-120℃,the test data are divided into training set and test set,and the random forest model is effectively verified to be suitable for the temperature calibration of NTC,and the temperature calibration accuracy reaches-0.015-0.02℃by modifying the model and optimizing the parameters.
万方数据
维普
