中文摘要：

采用基于最小二乘支持向量机（LS-SVM）的数据驱动方法建立高炉十字测温温度模型．首先通过对数据的相关性分析，选择与十字测温相关的输入变量；再用改进的智能优化算法（粒子群算法）来优化LS-SVM的参数，从而提高预测模型的精度；最后得到十字测温温度的LS-SVM预测模型．根据生产现场实际数据进行的实验表明，基于相关性分析的输入量选取能够在不影响预测精度的情况下降低计算复杂度；与常用的网格法相比，本文方法所建立的十字测温数据驱动模型精度提高3％，能够满足生产需要．

英文摘要：

Least square support vector machine （LS-SVM） is employed to model of the blast furnace cross temperature. First, correlation analysis is performed to select inputs related to cross temperature. Second, an improved particle swarm optimization （PSO） is proposed to obtain optimized parameters for LS-SVM in order to improve the prediction accuracy. Finally, the prediction model of blast furnace cross temperature based on LS-SVM is achieved. Experiments using practical production data illustrate that the input selection based on correlation analysis can reduce the computation complexity without influencing the prediction accuracy. Compared with the grid search method, the proposed data-driven cross temperature model represents an improvement in accuracy of 3%, and can meet the requirements of production.