Vertical Quench Furnace Hammerstein Fault Predicting Model Based on Least Squares Support Vector Machine and Its Application

摘要：

Since large-scale vertical quench furnace is voluminous, whose working condition is a typically complex process with distributed parameter, nonlinear, multi-inputs/multi-outputs, close coupled variables, etc, Hammerstein model of the furnace is presented. Firstly, the nonlinear function of Hammerstein model is constructed by least squares support vector machines regression. A numerical algorithm for subspace system (singular value decomposition, SVD) is utilized to identify the Hammerstein model. Finally, the model is used to predict the furnace temperature. The simulation research shows this model provides accurate prediction and is with desirable application value.

关键词： Hammerstein Model Least Squares Support Vector Machine (LS-SVM) System Identification Fault Diagnosis Large-scale Vertical Quench Furnace

作者: JIANG Shao-hua GUI Wei-hua YANG Chun-hua

作者单位: School of Information Science and Engineering, Central South University, 410083, Changsha, China Sch School of Information Science and Engineering, Central South University, 410083, Changsha, China

会议类型: 国际会议

会议名称: 2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)

会议地点: 广西桂林

会议语种:英文

页码: 203-207

在线出版日期: 2009-06-17（万方平台首次上网日期，不代表论文的发表时间）