中文摘要：

由于高炉中心温度较高,十字测温中心位置传感器极易损坏,并且更换周期长,因而导致无法及时判断炉顶煤气流分布.采用多输出支持向量回归（M-SVR）和随机权神经网络（RVFLNs）两种数据驱动智能建模方法建立高炉十字测温中心带温度估计模型,并基于实际工业数据对建立的模型进行验证和比较分析.结果表明,在样本数量较小时,M-SVR模型和RVFLNs模型都具有较好的温度估计效果,但当样本数量充足时,M-SVR模型的泛化性能和估计精度更优于RVFLNs模型.

英文摘要：

Due to the high temperature in the middle of blast furnace, the central position sensor of the cross temperature measuring is very easy to be damaged, and the replacement period is always long, resulting in the gas flow distribution not being observed in time. To this end, two kinds of data： based intelligent modeling methods of multi-output support vector regression machine （ M - SVR） and random vector functional-link networks （ RVFLNs） were used to establish the temperature estimation model of cross temperature measuring center of blast furnace. Finally, the temperature estimation model based on industrial data was verified and compared. The results show that both M - SVR model and RVFLNs model have good temperature estimation effect when the sample size is small. However, when the sample size is large enough, the generalization performance and estimation accuracy of M - SVR model is better than those of the RVFLNs model.