| 基于深度集成支持向量机的工业过程软测量方法 |
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| 引用本文： | 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. |
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| 作者姓名： | 马建 邓晓刚 王磊 |
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| 作者单位： | 中国石油大学(华东)信息与控制工程学院, 山东 青岛 266580 |
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| 基金项目： | 国家自然科学基金项目（61403418，21606256）；山东省自然科学基金项目（ZR2014FL016，ZR2016FQ21，ZR2016BQ14）；青岛市应用基础研究计划项目（16-5-1-10-jch）；中央高校基本科研业务费专项资金（17CX02054）。 |
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| 摘 要： | 基于支持向量机（SVM）的软测量建模方法已经在工业过程控制领域得到广泛应用，然而传统支持向量机直接针对原始测量变量建立模型，未能充分挖掘数据的内在特征信息以提高预测精度。针对该问题，本文提出一种基于深度集成支持向量机（DESVM）的软测量建模方法。该方法首先利用深度置信网络（DBN）来对数据进行深层次的信息挖掘，提取出数据的内在特征，然后引入基于Bagging算法的集成学习策略，构建基于深度数据特征的集成支持向量机模型，以提升软测量预测模型的泛化能力。最后通过数值系统和真实工业数据对方法进行应用分析，结果表明本文提出的方法能够有效提升支持向量机软测量模型的预测精度，能够更好地预测过程质量指标的变化。
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| 关 键 词： | 支持向量机 软测量 深度置信网络 集成学习 预测 |
| 收稿时间： | 2017-08-02 |
| 修稿时间： | 2017-10-19 |
Industrial process soft sensor method based on deep learning ensemble support vector machine |
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| MA Jian,DENG Xiaogang,WANG Lei.Industrial process soft sensor method based on deep learning ensemble support vector machine[J].Journal of Chemical Industry and Engineering(China),2018,69(3):1121-1128. |
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| Authors: | MA Jian DENG Xiaogang WANG Lei |
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| Affiliation: | College of Information and Control Engineering, China University of Petroleum, Qingdao 266580, Shandong, China |
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| 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. |
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| Keywords: | support vector machine soft sensor deep belief network ensemble learning prediction |
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