本文在仔细分析特征选择思想的基础上,将特征选择过程嵌入到学习机里面,提出了一种基于改进支持向量机的特征选择算法(Feature selection via Modified Support Vector Machines),该方法通过对特征的权重进行排序来实现特征选择.利用可...本文在仔细分析特征选择思想的基础上,将特征选择过程嵌入到学习机里面,提出了一种基于改进支持向量机的特征选择算法(Feature selection via Modified Support Vector Machines),该方法通过对特征的权重进行排序来实现特征选择.利用可以将特征选择过程和学习过程有机地统一起来,实验表明,与其它方法比较,该方法能够达到比较好的效果.展开更多
The endpoint parameters are very important to the process of EAF steel-making, but their on-line measurement is difficult. The soft sensor technology is widely used for the prediction of endpoint parameters. Based on ...The endpoint parameters are very important to the process of EAF steel-making, but their on-line measurement is difficult. The soft sensor technology is widely used for the prediction of endpoint parameters. Based on the analysis of the smelting process of EAF and the advantages of support vector machines, a soft sensor model for predicting the endpoint parameters was built using multiple support vector machines （MSVM）. In this model, the input space was divided by subtractive clustering and a sub-model based on LS-SVM was built in each sub-space. To decrease the correlation among the sub-models and to improve the accuracy and robustness of the model, the sub- models were combined by Principal Components Regression. The accuracy of the soft sensor model is perfectly improved. The simulation result demonstrates the practicability and efficiency of the MSVM model for the endpoint prediction of EAF.展开更多
文摘本文在仔细分析特征选择思想的基础上,将特征选择过程嵌入到学习机里面,提出了一种基于改进支持向量机的特征选择算法(Feature selection via Modified Support Vector Machines),该方法通过对特征的权重进行排序来实现特征选择.利用可以将特征选择过程和学习过程有机地统一起来,实验表明,与其它方法比较,该方法能够达到比较好的效果.
基金Item Sponsored by National Natural Science Foundation of China (60374003)
文摘The endpoint parameters are very important to the process of EAF steel-making, but their on-line measurement is difficult. The soft sensor technology is widely used for the prediction of endpoint parameters. Based on the analysis of the smelting process of EAF and the advantages of support vector machines, a soft sensor model for predicting the endpoint parameters was built using multiple support vector machines （MSVM）. In this model, the input space was divided by subtractive clustering and a sub-model based on LS-SVM was built in each sub-space. To decrease the correlation among the sub-models and to improve the accuracy and robustness of the model, the sub- models were combined by Principal Components Regression. The accuracy of the soft sensor model is perfectly improved. The simulation result demonstrates the practicability and efficiency of the MSVM model for the endpoint prediction of EAF.
