摘要
提出了一种基于KPCA-SVM的煤与瓦斯突出预测方法。该方法首先通过KPCA方法对影响煤与瓦斯突出的相关指标进行特征提取,然后利用SVM方法对煤与瓦斯突出进行分类预测。实例结果表明,该方法对煤与瓦斯突出预测的准确率明显高于直接运用SVM方法的煤与瓦斯突出预测准确率,且运算速度快,识别能力强,同时根据该方法建立的煤与瓦斯突出分类预测模型具有较好的稳定性和有效性。
The paper proposed a forecasting method of coal and gas outburst based on KPCA-SVM.The method firstly used KPCA to select features of correlative indexes influencing coal and gas outburst,then used SVM to make classified forecasting coal and gas outburst.The example forecasting result showed that the correctness rate of forecasting coal and gas outburst by the method is obviously higher than the one by SVM,and the method has quick operating speed and strong identification ability.Meanwhile,the forecasting model of coal and gas outburst on the basis of the method has good stability and validity.
出处
《工矿自动化》
2010年第10期36-38,共3页
Journal Of Mine Automation
