中国机械工程
中國機械工程
중국궤계공정
CHINA MECHANICAl ENGINEERING
2015年
15期
2056-2061
,共6页
何伟铭%水洪伟%宋小奇%甘屹%汪中厚%井原透
何偉銘%水洪偉%宋小奇%甘屹%汪中厚%井原透
하위명%수홍위%송소기%감흘%왕중후%정원투
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
傳感器分段標定%優化灰色 GM(1,1)模型%BP 神經網絡%麯線擬閤
전감기분단표정%우화회색 GM(1,1)모형%BP 신경망락%곡선의합
segmented calibration of transducer%optimized grey GM(1,1)model%BP neural net- work%curve fitting
针对大量程高精度传感器不能一次完成标定实验的情况,提出一种将优化灰色 GM(1,1)模型与 BP 神经网络相结合来预测分段标定过程中特征值缺失的方法,从而实现传感器的分段标定。首先,根据实验数据建立传统灰色 GM(1,1)模型,对待标定传感器和标准传感器的测量值进行缺失数据的预测；然后,为弱化传统灰色 GM(1,1)模型序列变化的幅度,提高模型的预测精度,利用中心逼近的思想对传统的GM(1,1)模型进行优化；最后,利用 BP 神经网络对优化的灰色 GM(1,1)残差序列进行修正,以较高的精度实现对分段标定过程中缺失特征值的预测。结果表明,待标定传感器和标准传感器组合预测模型的平均残差分别为0??023％和0??401％,证明了组合预测模型的有效性。所提出方法为解决大量程高精度传感器分段标定时静态特性曲线的拟合提供了一种新思路。
針對大量程高精度傳感器不能一次完成標定實驗的情況,提齣一種將優化灰色 GM(1,1)模型與 BP 神經網絡相結閤來預測分段標定過程中特徵值缺失的方法,從而實現傳感器的分段標定。首先,根據實驗數據建立傳統灰色 GM(1,1)模型,對待標定傳感器和標準傳感器的測量值進行缺失數據的預測；然後,為弱化傳統灰色 GM(1,1)模型序列變化的幅度,提高模型的預測精度,利用中心逼近的思想對傳統的GM(1,1)模型進行優化；最後,利用 BP 神經網絡對優化的灰色 GM(1,1)殘差序列進行脩正,以較高的精度實現對分段標定過程中缺失特徵值的預測。結果錶明,待標定傳感器和標準傳感器組閤預測模型的平均殘差分彆為0??023％和0??401％,證明瞭組閤預測模型的有效性。所提齣方法為解決大量程高精度傳感器分段標定時靜態特性麯線的擬閤提供瞭一種新思路。
침대대량정고정도전감기불능일차완성표정실험적정황,제출일충장우화회색 GM(1,1)모형여 BP 신경망락상결합래예측분단표정과정중특정치결실적방법,종이실현전감기적분단표정。수선,근거실험수거건립전통회색 GM(1,1)모형,대대표정전감기화표준전감기적측량치진행결실수거적예측；연후,위약화전통회색 GM(1,1)모형서렬변화적폭도,제고모형적예측정도,이용중심핍근적사상대전통적GM(1,1)모형진행우화；최후,이용 BP 신경망락대우화적회색 GM(1,1)잔차서렬진행수정,이교고적정도실현대분단표정과정중결실특정치적예측。결과표명,대표정전감기화표준전감기조합예측모형적평균잔차분별위0??023％화0??401％,증명료조합예측모형적유효성。소제출방법위해결대량정고정도전감기분단표정시정태특성곡선적의합제공료일충신사로。
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 caliG bration 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 transG ducer 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.
