关键词: 传感器分段标定, 优化灰色GM(1,1)模型, BP神经网络, 曲线拟合

Abstract:

As large range and high precision transducer could not complete the calibration with just one experiment, an integrated modeling method was proposed, which incorporated optimized grey GM(1,1) and BP neural network to predict the missing values in calibration, and the segmented calibration of transducer was realized. Firstly, according to experimental data, traditional grey GM(1,1) model was established to predict the missing values, which were measured by both calibrated transducer and standard transducer. In addition, in order to weaken the scope of the sequence and improve mode prediction accuracy, the idea of center approach was used to optimize traditional grey GM(1,1) model. Finally, BP neural network was applied for modifying the residuals of optimized grey GM (1,1), realizing the prediction of the missing values in calibration with a high accuracy. The results show that the residual mean of the combined model of calibrated and standard transducer are 0.023% and 0.401% respectively, the effectiveness of the combined predicting model is proved, so it can be used to predict the missing values for the segmented calibration of transducer, and a new method is proposed to solve characteristic curve fitting problem, which is related to segmented calibration of large range and high precision transducer.

Key words: segmented calibration of transducer, optimized grey GM(1,1) model, BP neural network, curve fitting