On-line least squares support vector machine algorithm in gas prediction

查看全文 作　　者：ZHAO Xiao-hu WANG Gang ZHAO Ke-ke TAN De-[1]jian

机构地区：[1]School of Information & Electronic Engineering, China University of Mining & Technology, Xuzhou, Jiangsu 221008, China

出　　处：《Mining Science and Technology》索引2009年第19卷第2期,共5页

摘　　要：Traditional coal mine safety prediction methods are off-line and do not have dynamic prediction functions.The Support Vector Machine(SVM) is a new machine learning algorithm that has excellent properties.The least squares support vector machine(LS-SVM) algorithm is an improved algorithm of SVM.But the common LS-SVM algorithm,used directly in safety predictions,has some problems.We have first studied gas prediction problems and the basic theory of LS-SVM.Given these problems,we have investigated the affect of the time factor about safety prediction and present an on-line prediction algorithm,based on LS-SVM.Finally,given our observed data,we used the on-line algorithm to predict gas emissions and used other related algorithm to compare its performance.The simulation results have verified the validity of the new algorithm. 关键词：支持向量机算法最小二乘支持向量机瓦斯预测在线安全预测机器学习算法 SVM 气体排放量

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On-line least squares support vector machine algorithm in gas prediction

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