【作者】 宋绍民； 张振飞； 王耀南；

【Author】 SONG Shaomin1,ZHANG Zhenfei1,WANG Yaonan2 1 Department of Electrical and Information Engineering,Hunan Institute of Technology,Hengyang 421002,P.R.China 2 College of Electrical and Information Engineering,Hunan University,Changsha 410082,P.R.China

【机构】 湖南工学院电气与信息工程系； 湖南大学电气与信息工程学院；

【摘要】 不同传感器具有不同的非线性.为提高传感器数据重构精度,本文提出两种数据重构方法.对于不是特别严重的非线性,采用最小二乘拟合与径向基函数残差插值进行融合重构,可以在增加有限计算量条件下提高数据的近似精度;对于非线性较严重的传感器,为兼顾局部特性,采用移动最小二乘法进行数据重构,它通过全局近似向局部近似的转化,同样使重构结果具有满意的近似精度.选用两种不同传感器进行实验,结果表明两种方法均行之有效.更多还原

【Abstract】 Sensors usually have different nonlinearity.This paper proposes two methods for improving sensors’data reconstruction.The first one is integrated by the least squares and the radical basis function interpolation,which is aimed to the slight nonlinear sensors,and under the limited increament of computation,it can make the results more precise.The second one is used for serious nonlinearity and based on the moving least squares which converts the approximation generally into locally,and the result of this way is also satisfied.By choosing two typical sensors for testing,the efficiency of the two proposed methods were proved.更多还原