高炉铁水硅含量预报的ICA-SVM建模方法

提出了一种基于改进的动态独立分量分析（independent component analysis, ICA）和支持向量机(support vector machine, SVM)的高炉铁水硅含量预报模型建模方法．采用动态ICA方法对样本数据进行特征提取，消除生产工艺参数之间的相关性．在此基础上，再使用目前计算复杂性较小的最小二乘SVM算法建立高炉铁水硅含量预报的动态递推模型，并引入了遗传算法以优化模型性能．以某钢厂高炉实际生产数据进行了应用实验，并与现有的时间序列分析、人工神经网络和基本SVM建模方法进行了对比．实验统计结果表明，本文方法显著提高了铁水硅含量的预测命中率．

ICA-SVM-Based Modeling for Predicting Silicon Content in Blast Furnace Hot Metal

Based on the improved dynamic independent component analysis(ICA) and the support vector machine(SVM),a modeling method for the prediction of silicon content in blast furnace hot metal is proposed in this paper.In order to eliminate the correlations among production parameters,the dynamic ICA is used for feature extraction.With the help of least square SVM which has low computational complexity,a dynamic recursive model is then built for the prediction of silicon content in hot metal,and a genetic algorithm...