摘要
高炉冶炼是个具有高度复杂性、混沌性、时滞性的动态过程,工业上常常用铁水硅含量反馈高炉炉温热状态波动变化,而偏态投影深度在数据有偏时可以较好地反映出数据的离群情况,在高维数据分类计算中十分稳健.首先,通过差分处理及相关性分析确定11个影响因素作为输入变量,用于研究各变量变化对硅含量变化的关系;然后,将偏态投影深度值在90%的置信区间外的数据视作离群值,分为稳定类和离群类;最后,对稳定数据利用Elman神经网络预测模型进行预测,对于离群类利用Logistic模型在炉温不同波动方向下的规律进行归类预测.实例仿真研究表明,稳定类157炉的预测精度高达85.3%,离群类的预测精度达到82.6%.
Blast furnace smelting is a dynamic process with high complexity,chaos and time delay.In industry,molten iron silicon content is often used to feed back the fluctuation of blast furnace temperature and thermal state.The skew projection depth can reflect the outliers of the data well when the data is biased,and it is very robust in the classification calculation of high-dimensional data.Firstly,11 influencing factors are determined as input variables through differential processing and correlation analysis in this paper,which are used to study the relationship between changes of various variables and changes of silicon content.Then,the data whose projection depth value is outside 90%confidence interval are regarded as outliers and classified into stable and outliers.Finally,the stable data are predicted by Elman neural network prediction model,and the outliers are classified and predicted by Logistic model under different fluctuation directions of furnace temperature.The simulation results show that the prediction accuracy of stable class 157 furnace is up to 85.3%,and that of outlier class is up to 82.6%.
作者
罗世华
陈坤
LUO Shi-hua;CHEN Kun(College of Statistics,Jiangxi University of Finance and Economics,Nanchang 330013,China)
出处
《控制与决策》
EI
CSCD
北大核心
2021年第2期491-497,共7页
Control and Decision
基金
国家自然科学基金项目(61973145)
江西省教育厅重点项目(GJJ180247).
