高炉透气性指数是生产中最重要的监控参量之一，对透气性指数未来趋势的把握，于高炉操作者而言至关重要。通过支持向量机结合小波分析建立一个高炉透气性指数的预测模型, 将历史四点透气性指数通过7层小波分解，使其波动范围变窄，结合相关的操作参数针对分解后的8个小波分量通过支持向量机建立8个预测子模型，最后通过预测分量的重构得到预测值。模型四点预测误差较小，并能满足高炉短期调节时限的要求。

The permeability index for blast furnaces is an important monitoring parameter in their operation. Proper trend prediction of the permeability index is important for good operation. Support vector machines (SVM) combined with wavelet analysis are adopted to build a forecasting model. Four historic values of a permeability index are analyzed by a wavelet analysis via seven levels. Based on eight wavelet analyzed values and combined with operating parameters, eight submodels are built using the least square support vector machines method. The prediction components are reconstructed to obtain a forecast. The details of modeling, validation and result analyses are presented.

国家自然科学基金和上海宝钢集团公司联合资助(50374085)