Hot Metal Desulphurization Control Model Based on PCA-RBFNN

摘要：

Hot metal desulphurization pretreatment process has characteristics of multivariate and nonlinear, the sulfur content of hot metal can not be monitored online, it’s difficult to set the amount of desulfurating agent accurately. To solve this problem, a model of RBF neural network was proposed based on production data of desulphurization. The model improved quality of the modeling data by removing outliers with similarity coefficient method. Simplified the model structure and reduced data noise by eliminating correlation of the modeling data with PCA method. Improved generalization ability with subtractive clustering algorithm and improved network error learning function. Simulation results show that the model is high accuracy and can determine the amount of desulfurating agent accurately.

关键词： Desulphurization Data processing PCA RBF neural network

作者: Yukun Wang Yong Zhang

作者单位: School of electronic & information engineering, University of Science and Technology Liaoning, Anshan 114051

会议类型: 国际会议

会议名称: 2011 China Control and Decision Conference(2011中国控制与决策会议 CCDC)

会议地点: 四川绵阳

会议语种:英文

页码: 3054-3057

在线出版日期: 2011-05-23（万方平台首次上网日期，不代表论文的发表时间）