The Operating Conditions Prediction of Electric Arc Furnace Based on Least Squares Support Vector Machines
As the electric arc furnace (EAF)operating conditions is difficult to directly measured, resulting in the EAF not easly to determine the various operating conditions. In view of these problems, based on the characteristics of the EAF ferroalloy smelting, this method about the use of multi-scale decomposition of the nuclear functions of the least square support vector machines (LS-SVM) is proposed to predict the operating conditions of the EAF, this method can keep the general approximation ability of curve, meanwhile it improves the approximation ability in the local area model. The experiment results show that it has superior characteristics in the respect of prediction precision and prediction speed and the operating conditions can be predicted accurately and timely, both being economic of production cost and increasing.
east squares support vector machine scaling kernel operating conditions prediction electric arc furnace(EAF)
ZHANG De-jiang ZHANG Guang-lai ZHANG Niao-na
Institute of Electrical and Electronic Engineering Changchun University of Technology Changchun, Chi Changchun University of Technology Institute of Electrical and Electronic Engineering Changchun, Chi
国际会议
长春
英文
263-266
2010-08-24（万方平台首次上网日期，不代表论文的发表时间）