The endpoint parameters are very important to the process of EAF steel-making, but their on-line measurement is difficult. The soft sensor technology is widely used for the prediction of endpoint parameters. Based on the analysis of the smelting process of EAF and the advantages of support vector machines, a soft sensor model for predicting the endpoint parameters was built using multiple support vector machines (MSVM). In this model, the input space was divided by subtractive clustering and a sub-model based on LS-SVM was built in each sub-space. To decrease the correlation among the sub-models and to improve the accuracy and robustness of the model, the sub-models were combined by Principal Components Regression. The accuracy of the soft sensor model is perfectly improved. The simulation result demonstrates the practicability and efficiency of the MSVM model for the endpoint prediction of EAF.
参考文献
| [1] | R. D. MORALES;H. RODRIGUEZ-HERNANDEZ;P. GARNICA-GONZALEZ .A Mathematical Model for the Reduction Kinetics of Iron Oxide in Electric Furnace Slags by Graphite Injection[J].ISIJ International,1997(11):1072-1080. |
| [2] | Johannes Gerhardt BEKKER .Modeling and Simulation of an Electric Arc Furnace Process[J].ISIJ International,1999(1):23-32. |
| [3] | ZHANG Jun-jie .Terminal Adaptive Predication and Expert Directing Operation for the Steelmaking Process of Electric Arc Furnace[J].ACTA AUTOMATICA SINICA,1993,19(04):463-467. |
| [4] | 刘锟,刘浏,何平,武辉斌,马兆红.增量神经网络模型预报100 t电弧炉终点碳、磷和温度的应用[J].特殊钢,2004(03):40-41. |
| [5] | 张学工.关于统计学习理论与支持向量机[J].自动化学报,2000(01):32-42. |
| [6] | 王定成,方廷健,高理富,马永军.支持向量机回归在线建模及应用[J].控制与决策,2003(01):89-91,95. |
| [7] | Dasaratha VS;Richard CS .EricBB Process Modeling Using Stacked Neural Networks[J].AICHE Journal,1996,42(09):89-95. |
| [8] | Cho S B;Kim J H .Combining Multiple Neural Networks by Fuzzy Integral for Recognition[J].IEEE Transon System Man and Cybern,1995,25(02):380-384. |
| [9] | WANG Xu-dong;SHAO Hui-he;LUO Rong-fu .The Distributed RBF Neural Network and Its Application in Soft Sensor[J].Control Theory and Applications,1998,15(04):558-563. |
| [10] | 熊智华,王雄,徐用懋.一种利用多神经网络结构建立非线性软测量模型的方法[J].控制与决策,2000(02):173-176. |
| [11] | 常玉清,王福利.基于模糊规则分类的分布式RBF网络软测量模型[J].计量学报,2002(02):131-133,150. |
| [12] | Chiu S L .Fuzzy Model Identification Based on Cluster Estimation[J].Journal of Intelligent Fuzzy Systems,1994,2(03):267-278. |
| [13] | Suykens J A K;Vandewalle J .Least Squares Support Vector Machines Classifiers[J].Neural Processing Letters,1999,19(03):293-300. |
| [14] | 阎威武,朱宏栋,邵惠鹤.基于最小二乘支持向量机的软测量建模[J].系统仿真学报,2003(10):1494-1496. |
| [15] | K. Demirli;S.X. Cheng;P. Muthukumaran .Subtractive clustering based modeling of job sequencing with parametric search[J].Fuzzy sets and systems,2003(2):235-270. |
| [16] | 阎辉,张学工,马云潜,李衍达.基于变异函数的径向基核函数参数估计[J].自动化学报,2002(03):450-455. |
- 下载量()
- 访问量()
- 您的评分：
-
10%
-
20%
-
30%
-
40%
-
50%