Error Prediction Method for Temperature Sensor Based on Multi Model Fusion

摘要

In the process of high altitude detection, due to the hysteresis, solar radiation and other factors, there is a deviation between the measured data of temperature sensor and the standard data. To solve this problem, we combine depth neural network, wavelet function, SVM and XGBoost to propose an error prediction model. Morlet wavelet is used as the activation function of neural network to improve the prediction ability. The stacking integrated learning method is used to build a cascade prediction model to achieve extreme gradient promotion. By collecting the real data of meteorological observation, the dataset is established, and the proposed method is evaluated on this dataset. The experimental results show that compared with the traditional model, the improved model has certain effectiveness, MSE reduces 0.173, effectively overcomes the influence of solar radiation, and improves the measurement accuracy of the sensor. Moreover, this method has strong generalization and can be easily extended to other data prediction and regression tasks.