摘要
为了提高煤炭自燃危险性预测精度,提出了基于KPCA-Fisher判别分析的煤炭自燃预测模型。利用核主成分分析法(KPCA)对相关程度较高的特征指标进行非线性特征提取,将提取出的主成分作为Fisher判别模型的判别因子。选取宣东2号煤矿煤炭自燃的历史数据,以3∶1的比例抽取训练集和测试集并代入该模型进行训练和测试,并将预测结果与传统的FDA、SVM和BPNN模型相比较。结果表明:KPCA能有效提取煤炭自燃特征指标,降低指标间信息冗余,基于KPCA的Fisher判别模型用于煤炭自燃预测简单可行,准确率较高。
In order to improve the prediction accuracy of coal spontaneous combustion,a model based on KPCA-Fisher discriminant analysis was proposed to predict coal spontaneous combustion,kernel principal component analysis(KPCA)was used tonon-linear feature extraction for characteristic indexes with higher correlation.The extracted principal components were used as the discriminant factor of Fisher discriminant model.The historical data of coal spontaneous combustion in No.2 Coal Mine of Xuandong was selected,and the model was trained and tested by extracting training set and test set with the ratio of 3:1 and the forecast results were compared with traditional FDA,SVM,BPNN method.The results showed that KPCA can extract the characteristic indexes of coal spontaneous combustion effectively,and reduce the information redundancy among the indexes.Using Fisher discriminant model based on KPCA to forecast coal spontaneous combustion is not only simple and feasible,but also with high accuracy.
作者
温廷新
于凤娥
WEN Tingxin;YU Feng’e(System Engineering Institute,Liaoning Technical University,Huludao 125105,China)
出处
《矿业安全与环保》
北大核心
2018年第2期49-53,58,共6页
Mining Safety & Environmental Protection
基金
国家自然科学基金项目(713711091)
辽宁省社科基金项目(L14BTJ004)
关键词
煤炭自燃
预测
核主成分分析
FISHER判别分析
回代估计法
coal spontaneous combustion
prediction
kernel principal component analysis
Fisher discriminant analysis
backgeneration estimation
