化工学报2018，Vol.69Issue(3)：1121-1128,8.DOI:10.11949/j.issn.0438-1157.20171050
基于深度集成支持向量机的工业过程软测量方法
Industrial process soft sensor method based on deep learning ensemble support vector machine
摘要
Abstract
The soft sensor modeling method based on support vector machine (SVM) has been widely used in the field of industrial process control. However, the traditional support vector machine directly models the original measurement variables without fully extracting the intrinsic data information to improve the prediction accuracy. Aiming at this problem, a soft sensor modeling method based on deep ensemble support vector machine (DESVM) is proposed in this paper. Firstly, this method uses the deep belief network (DBN) to carry on the deep information mining, and extracts the intrinsic data characteristic. Then the ensemble learning strategy based on the Bagging algorithm is introduced to construct the ensemble support vector machine model based on the deep data characteristic, which can enhance generalization ability of soft measurement prediction model. Finally, the applications on a numerical system and real industrial data are used to validate the proposed method. The results show that the proposed method can effectively improve the prediction accuracy of the soft vector model of support vector machine and can predict the change of process quality index better.关键词
支持向量机/软测量/深度置信网络/集成学习/预测Key words
support vector machine/soft sensor/deep belief network/ensemble learning/prediction分类
信息技术与安全科学引用本文复制引用
马建,邓晓刚,王磊..基于深度集成支持向量机的工业过程软测量方法[J].化工学报,2018,69(3):1121-1128,8.基金项目
国家自然科学基金项目(61403418,21606256) （61403418,21606256）
山东省自然科学基金项目(ZR2014FL016,ZR2016FQ21,ZR2016BQ14) （ZR2014FL016,ZR2016FQ21,ZR2016BQ14）
青岛市应用基础研究计划项目(16-5-1-10-jch) （16-5-1-10-jch）
中央高校基本科研业务费专项资金(17CX02054).supported by the National Natural Science Foundation of China(61403418,21606256),the Natural Science Foundation of Shandong Province(ZR2014FL016,ZR2016FQ21,ZR2016BQ14),the Application and Fundamental Research Project of Qingdao(16-5-1-10-jch)and the Fundamental Research Funds for the Central Universities(17CX02054). （17CX02054）
