| On-line forecasting model for zinc output based on self-tuning support vector regression and its application |
| |
| 作者姓名： | 胡志坤 桂卫华 彭小奇 |
| |
| 作者单位： | [1]SchoolofPhysicsScienceandTechnology,CentralSouthUniversity,Changsha410083,China／／SchoolofInformationScienceandEngineering,CentralSouthUniversity,Changsha410083,China [2]SchoolofInformationScienceandEngineering,CentralSouthUniversity,Changsha410083,China [3]SchoolofPhysicsScienceandTechnology,CentralSouthUniversity,Changsha410083,China |
| |
| 基金项目： | 国家重点基础研究发展计划(973计划) |
| |
| 摘 要： | An on-line forecasting model based on self-tuning support vectors regression for zinc output was put forward to maximize zinc output by adjusting operational parameters in the process of imperial smelting furnace. In this model, the mathematical model of support vector regression was converted into the same format as support vector machine for classification. Then a simplified sequential minimal optimization for classification was applied to train the regression coefficient vector α-α^* and threshold b. Sequentially penalty parameter C was tuned dynamically through forecasting result during the training process. Finally, an on-line forecasting algorithm for zinc output was proposed. The simulation result shows that in spite of a relatively small industrial data set, the effective error is less than 10% with a remarkable performance of real time. The model was applied to the optimization operation and fault diagnosis system for imperial smelting furnace.
|
| 关 键 词： | 密闭铅锌鼓风炉 支持向量回归 顺序最佳化 锌产量 在线预测 |
| 收稿时间： | 19 September 2003 |
| 修稿时间： | 19 December 2003 |
On-line forecasting model for zinc output based on self-tuning support vector regression and its application |
| |
| Hu Zhi-kun , Gui Wei-hua and Peng Xiao-qi.On-line forecasting model for zinc output based on self-tuning support vector regression and its application[J].Journal of Central South University of Technology,2004,11(4):461-464. |
| |
| Authors: | Hu Zhi-kun Gui Wei-hua and Peng Xiao-qi |
| |
| Affiliation: | (1) School of Physics Science and Technology, Central South University, 410083 Changsha, China;(2) School of Information Science and Engineering, Central South University, 410083 Changsha, China |
| |
| Abstract: | An on-line forecasting model based on self-tuning support vectors regression for zinc output was put forward to maximize zinc output by adjusting operational parameters in the process of imperial smelting furnace. In this model, the mathematical model of support vector regression was converted into the same format as support vector machine for classification. Then a simplified sequential minimal optimization for classification was applied to train the regression coefficient vector α- α* and threshold b. Sequentially penalty parameter C was tuned dynamically through forecasting result during the training process. Finally, an on-line forecasting algorithm for zinc output was proposed. The simulation result shows that in spite of a relatively small industrial data set, the effective error is less than 10% with a remarkable performance of real time. The model was applied to the optimization operation and fault diagnosis system for imperial smelting furnace. |
| |
| Keywords: | imperial smelting furnace support vectors regression sequential minimal optimization zinc output on-line forecasting |
| 本文献已被 CNKI 维普 万方数据 SpringerLink 等数据库收录！ |
