Using LSSVM Model to Predict the Silicon Content in Hot Metal Based on KPCA Feature Extraction
To overcome the difficulty that silicon content in hot metal can not be effectively controlled in ironmaking process due to lack of real-time on-line instrumentation, a prediction method is proposed by combining the Kernel Principal Component Analysis(KPCA) with the Least Square Support Vector Machine(LSSVM). Using KP CA as a preprocessor of LSSVM to extract the principal features of original data and employ the 10-fold cross validation to optimize the parameters of LSSVM. Then LSSVM is applied to proceed silicon content regression modeling. KPCA can denoise the input data and capture the high-ordered nonlinear principal components in input data space, and with LSSVM we can establish a prediction model between the featured principal components and the primary variable for the silicon content in iron making processes. The data of the model are collected from No.6 Blast Furnace in Baotou Iron and Steel Group Co. of China. The results show that the LSSVM model based on KPCA feature selection has higher accuracy and better tracking performance compared with LSSVM or PCA-LSSVM models, so the proposed method can satisfy the requirements of on-line measurements of silicon content in hot metal.
KPCA LSSVM Silicon content in hot metal Prediction
Yikang Wang Chuanhou Gao Xiangguan Liu
Department of Mathematics, Zhejiang University, Hangzhou, 310027, China
国际会议
2011 China Control and Decision Conference(2011中国控制与决策会议 CCDC)
四川绵阳
英文
1967-1971
2011-05-23（万方平台首次上网日期，不代表论文的发表时间）