Blended coal’s property prediction model based on PCA and SVM

被引:0
作者
Yan-bin Cui
Cheng-shui Liu
机构
[1] North China Electric Power University,Department of Mechanical Engineering
[2] Beijing City University,Digital City Institute
来源
Journal of Central South University of Technology | 2008年 / 15卷
关键词
prediction model; blended coal’s property; support vector machine; principal component analysis;
D O I
暂无
中图分类号
学科分类号
摘要
In order to predict blended coal’s property accurately, a new kind of hybrid prediction model based on principal component analysis (PCA) and support vector machine (SVM) was established. PCA was used to transform the high-dimensional and correlative influencing factors data to low-dimensional principal component subspace. Well-trained SVM was used to extract influencing factors as input to predict blended coal’s property. Then experiments were made by using the real data, and the results were compared with weighted averaging method (WAM) and BP neural network. The results show that PCA-SVM has higher prediction accuracy in the condition of few data, thus the hybrid model is of great use in the domain of power coal blending.
引用
收藏
页码:331 / 335
页数:4
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