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

来源期刊：中南大学学报(英文版)2008年第z2期

论文作者：崔彦彬 刘承水

文章页码：331 - 335

Key words：prediction model； blended coal’s property； support vector machine； principal component analysis

Abstract: 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.