为了提高热电偶动态校准的准确性, 采用半导体激光器、红外探测器和被校准热电偶组成新的热电偶动态校准系统, 分析了动态校准中红外探测器静态校准目的。根据反向传播神经网络原理, 确定了反向传播神经网络的结构和参量, 同时针对普通K型铠装热电偶进行了动态校准实验, 得到红外探测器静态校准数据,由此数据采用最小二乘法和反向传播神经网络分别进行数据的非线性拟合, 对两种方法的拟合结果进行了分析, 并给出了拟合曲线。结果表明, 在样本数据少、分布不均匀的情况下, 反向传播神经网络拟合效果优于传统的最小二乘法, 减小了由于数据拟合所带来的误差, 能够更加准确地获得热电偶动态特性, 实现热电偶动态补偿。这一研究结果对于热电偶动态特性研究具有重要的参考价值。

In order to improve the accuracy of thermocouple dynamic calibration, based on a new thermocouple dynamic calibration system composed of a semiconductor laser, infrared detector and calibration thermocouples, the static calibration of the infrared detector was analyzed in the dynamic calibration system. According to the principle of back propagation(BP) neural network, the structure and the parameters of neural network were determined. The dynamic calibration experiment with ordinary K type armor-loaded thermocouple was performed and the static calibration data of infrared detector was acquired. Nonlinear curve fitting was performed using the least square method and BP neural network. The fitting results of the both the methods were analyzed and the fitting curves were obtained. The results show that, the fitting effect of BP neural network is better than the traditional least square method when there is less and uneven distributed data. The error caused by data fitting is reduced, dynamic characteristics of the thermocouple are acquired more accurately and thermocouple dynamic compensation is realized. The study has an important reference to the research of thermocouple dynamic characteristics.