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《Computer Engineering and Design》 2010-01
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Gross error detection of soft sensing data based on 3MAD-PCA

HU Yun-ping,ZHAO Ying-kai(School of Automation,Nanjing University of Technology,Nanjing 210009,China)  
Classical PCA is a method of detecting gross error for soft sensor modeling data.But this method performs badly when there is great error in the member univariate variance because the PCs(principal components) are not obtained precisely.A new method called 3MAD-PCA is presented combined with univariate detecting method.The new method firstly detects univariate error with 3MAD,then multivariate error is detected with classical PCA,as a result,the stability of classical PCA and detect gross error are improved effectively.The method is used to detect gross errors in modeling data for a propylene concentration soft sensor and good results are obtained.
【Fund】: 国家863高技术研究发展计划重点基金项目(2006AA040308-02)
【CateGory Index】: TP274
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Chinese Journal Full-text Database 1 Hits
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