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基于样条曲线插值的压力传感器的温度补偿
引用本文:樊尚春,张秋利,秦杰. 基于样条曲线插值的压力传感器的温度补偿[J]. 北京航空航天大学学报, 2006, 32(6): 684-686
作者姓名:樊尚春  张秋利  秦杰
作者单位:北京航空航天大学 仪器科学与光电工程学院, 北京 100083
摘    要:提出了基于三次样条曲线插值的温度补偿方法,用这种方法对测压范围为0.013 3×105~3.198 9×105 Pa,温度应用范围为-55~+80℃的高精度谐振筒压力传感器的实验标定结果进行了温度补偿.为加快标定过程,给出了传感器标定点数的减少方案.结果表明,在传感器的标定点数减少2/3的情况下,提出的温度补偿方法的综合误差为0.007 9%,约是基于径向基函数(RBF)神经网络的温度补偿方法的1/2,从而有效减少了传感器的标定成本和工作量.这对于解决高精度压力传感器的温度补偿问题具有一定的理论意义和工程应用价值. 

关 键 词:传感器   样条   温度   补偿   神经网络
文章编号:1001-5965(2006)06-0684-03
收稿时间:2005-07-01
修稿时间:2005-07-01

Temperature compensation of pressure sensor based on the interpolation of splines
Fan Shangchun,Zhang Qiuli,Qin Jie. Temperature compensation of pressure sensor based on the interpolation of splines[J]. Journal of Beijing University of Aeronautics and Astronautics, 2006, 32(6): 684-686
Authors:Fan Shangchun  Zhang Qiuli  Qin Jie
Affiliation:School of Instrument Science and Opto-electronics Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100083, China
Abstract:The temperature compensation method based on the interpolation of cubic splines was presented. This method was applied for the temperature compensation of the experimental results of calibration of high precision resonant cylinder pressure sensors. The measuring range of the pressure sensor is 0.013 3×105~3.198 9×105 Pa and the applied temperature range is -55~+80℃. To make the calibrating process of pressure sensor fast, the principle of reducing the number of calibrating points was presented. The results indicate that the integrated error of the presented method of temperature compensation is 0.007 9%, when the number of calibrating points reduces about 2/3. The integrated error is approximately half of the method of temperature compensation based on the radial basis function(RBF)neural network. Thereby the presented method reduces the cost and workload of calibration effectually. The proposed method can provide a valuable theoretical reference for the temperature compensation of the high precision pressure sensors. 
Keywords:sensors  splines  temperature  compensation  neural network
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