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《Journal of Chemical Engineering of Chinese Universities》 2010-04
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New Method for Robust Data Reconciliation Based on Biweight M-Estimator

JIN Si-yi,LIU Meng,LI Ning,TAO Shao-hui (College of Chemical Engineering,Qingdao University of Science and Technology,Qingdao 266042,China)  
Reliable and accurate process measurements are crucial for the control,simulation and management of the process.However,gross errors existing in the measured process data can severely bias the reconciled data.The existing robust methods are not very efficient to eliminate the influence of gross errors completely.Based on the mechanism of the Biweight M-Estimator,a new target function with strong robustness was proposed,which nullifies the effect of gross errors on data reconciliation by limiting the function values of variables with gross error to a constant.The performance of the proposed method and its comparison with Huber's method and the method based on the Cauchy distribution were illustrated through a linear problem and a nonlinear one.The new method gives promising results for data reconciliation and gross errors detection with more stable property.Therefore,the proposed method should be chosen with first priority for data reconciliation.
【Fund】: 重质油国家重点实验室开放课题基金资助项目(2008-08)
【CateGory Index】: TQ015.9
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