偏最小二乘法在传感器误差补偿中的应用  

Application of Partial Least Squares in Sensor Error Compensation

作　　者：徐建忠[1] 张彦超[1] 李永生[1] 于广浩[1] 苏奎[1] XU Jianzhong;ZHANG Yanchao;LI Yongsheng;YU Guanghao;SU Kui(Mudanjiang Medical University,Mudanjiang Heilongjiang 157011)

机构地区：[1]牡丹江医学院,黑龙江牡丹江157011

出　　处：《软件》2021年第2期75-77,共3页Software

基　　金：2019年度黑龙江省属高校基本科研业务费科研项目(项目编号:2019-KYYWF-0990)。

摘　　要：在实际测量中由于湿度温度等不确定性使得传感器数据出现无法避免的随机性误差。为了减小误差,一种快捷简便的处理方式是使用最小二乘法对数据进行线性回归修正。线性补偿的方式能解决很多传感器测量对于噪声等随机现象出现的误差,但通常测量数据的实际函数本身都是非线性的,用线性函数来模拟非线性的测量数据往往会出现精度不足的问题。为此,本文设计了一种增加自变量阶次及变量系数的方式来提高补偿精度,由于增加的变量系数可能导致多重相关性等问题,改用单因变量偏最小二乘法来建立补偿模型。In practice,uncertainties such as humidity and temperature cause unavoidable random errors in the sensor data.In order to reduce the error,a quick and easy way to deal with it is to use the least squares method to correct the data by linear regression.The linear compensation method can solve many sensor measurement errors for random phenomena such as noise,but usually the actual function of the measurement data itself is nonlinear,and using a linear function to simulate nonlinear measurement data often results in a lack of accuracy.For this reason,this paper designs a way to increase the order of independent variables and variable coefficients to improve the compensation accuracy,and because the increased variable coefficients may lead to problems such as multiple correlations,a single dependent variable partial least squares method is used instead to build the compensation model.

关 键 词：传感器数据 数据补偿 偏最小二乘 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]