| 基于支持向量机(SVM)的工业过程辨识 |
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| 引用本文： | 李丽娜,侯朝桢.基于支持向量机(SVM)的工业过程辨识[J].北京理工大学学报,2003,23(5):569-570. |
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| 作者姓名： | 李丽娜 侯朝桢 |
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| 作者单位： | 北京理工大学,信息科学技术学院自动控制系,北京,100081 |
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| 基金项目： | 国家部级科研项目;02306005; |
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| 摘 要： | 将支持向量机应用到典型的时变、非线性工业过程——连续搅拌反应釜的辨识中，并与BP神经网络建模相比较，仿真结果表明了支持向量机的有效性与优越性．支持向量机以其出色的学习能力为工业过程的辨识提出了一种新的途径。
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| 关 键 词： | 支持向量机 工业过程辨识 回归 |
| 文章编号： | 1001-0645(2003)05-0569-02 |
| 收稿时间： | 2002/9/25 0:00:00 |
Identification of Industrial Processes Based on Support Vector Machines |
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| LI Li na and HOU Chao zhen.Identification of Industrial Processes Based on Support Vector Machines[J].Journal of Beijing Institute of Technology(Natural Science Edition),2003,23(5):569-570. |
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| Authors: | LI Li na and HOU Chao zhen |
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| Institution: | Department of Automatic Control, School of Information Science and Technology, Beijing Institute of Technology, Beijing100081, China;Department of Automatic Control, School of Information Science and Technology, Beijing Institute of Technology, Beijing100081, China |
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| Abstract: | Industrial processes are generally time varied and nonlinear, and it is difficult to acquire data for them. Support vector machine (SVM) provides a new mode for industrial processes identification due to its excellent learning capability. In this paper, SVM is applied to the identification of continuous stirred tank reactor (CSTR). Compared with BP neural network, the simulation results show the effectiveness and superiority of SVM. |
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| Keywords: | support vector machine identification of industrial process regression |
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