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基于FA-SVR 的丙酮精制产品质量软测量模型
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2009-05-25
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针对某丙酮精制过程，提出采用FA 与SVR 相结合的方法建立丙酮产品质量的软测量模型。采用因子分析（FA）方法提取辅助变量的特征信息，并消除各变量之间的相关性，然后利用支持向量回归（SVR）建立丙酮产品质量指标的软测量模型。在实际生产过程数据上进行了仿真实验，并与传统的稳健回归分析及神经网络等方法进行了比较，结果表明本方法具有良好的预测效果。
关键词：软测量；支持向量回归；因子分析；丙酮精制
FA-SVR based Soft-Sensor Model of Product Quality in Acetone Refining Duan Ying, Fan Quanyi, Xiong Zhihua (Department of Automation, Tsinghua University, Beijing 100084) Abstract: A soft-sensor model of product quality in acetone refining process is proposed by combining factor analysis (FA) and support vector regression (SVR). During the data processing, FA is applied to extract the characteristics of secondary variables and eliminate the collinearity among these variables. The extracted principal factors are then used as input parameters of soft sensor model, and the model is built by using SVR to predict the acetone quality. The experiment results on the real industrial data show that the method of FA-SVR is more effective than robust regression and neural network methods.
Keywords: Soft sensor; Support vector regression (SVR); Factor Analysis (FA) ; Acetone refining
关键词：软测量；支持向量回归；因子分析；丙酮精制
FA-SVR based Soft-Sensor Model of Product Quality in Acetone Refining Duan Ying, Fan Quanyi, Xiong Zhihua (Department of Automation, Tsinghua University, Beijing 100084) Abstract: A soft-sensor model of product quality in acetone refining process is proposed by combining factor analysis (FA) and support vector regression (SVR). During the data processing, FA is applied to extract the characteristics of secondary variables and eliminate the collinearity among these variables. The extracted principal factors are then used as input parameters of soft sensor model, and the model is built by using SVR to predict the acetone quality. The experiment results on the real industrial data show that the method of FA-SVR is more effective than robust regression and neural network methods.
Keywords: Soft sensor; Support vector regression (SVR); Factor Analysis (FA) ; Acetone refining
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