基于聚类-二叉树支持向量机的砂土液化预测模型
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摘要
建立在统计学习理论基础之上的支持向量机(SVM),是一种基于结构风险最小的小样本机器学习方法。经典的支持向量机主要针对二分类问题,而工程实践中遇到的往往是多分类问题。根据影响砂土液化的主要因素,采用聚类分析中的类距离思想,建立了基于聚类-二叉树的多类支持向量机的砂土液化判别模型。该模型可以通过有限样本的学习,建立砂土液化与各影响因素之间的非线性关系。研究结果表明,基于聚类-二叉树支持向量机的层次结构合理,分类精度高,泛化性好,可对砂土液化等级进行较准确判别。
Support vector machine(SVM) is a novel and powerful learning method which is derived based on statistical learning theory(SLT) and the structural risk minimization principle.Traditional SVM only deals with the binary classification problems,however,there are large numbers of multi-classification problems in practical engineering.With a view to the main factors with important influence on sand liquefaction,multi-class support vector machines model based on clustering-binary tree is established.The nonlinear relationship between sand liquefaction and influencing factors is obtained from the finite empirical data by SVM.The results indicate that the structure of model base on SVM is reasonable;this algorithm is feasible;and it can predict the sand liquefaction more accurately.
Support vector machine(SVM) is a novel and powerful learning method which is derived based on statistical learning theory(SLT) and the structural risk minimization principle.Traditional SVM only deals with the binary classification problems,however,there are large numbers of multi-classification problems in practical engineering.With a view to the main factors with important influence on sand liquefaction,multi-class support vector machines model based on clustering-binary tree is established.The nonlinear relationship between sand liquefaction and influencing factors is obtained from the finite empirical data by SVM.The results indicate that the structure of model base on SVM is reasonable;this algorithm is feasible;and it can predict the sand liquefaction more accurately.
引文
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[2]余跃心.砂土液化判别方法可靠性评价[J].岩土力学,2004,25(5):803-807.SHE Yue-xin.Probability evaluation of liquefaction distinguishing method of sands[J].Rock and Soil Mechanics,2004,25(5):803-807.
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[4]赵洪波.围岩破坏模式识别的支持向量机研究[J].岩土力学,2005,26(2):235-238.ZHAO Hong-bo.Recognition of collapse type of surrounding rock mass based on support vector machine[J].Rock and Soil Mechanics,2005,26(2):235-238.
[5]孟媛媛,刘希玉.一种新的基于二叉树的SVM多类分类方法基[J].计算机应用,2005,25(11):2653-2657.MENG Yuan-yuan,LIU Xi-yu.A new SVM multiclass classification based on binary tree[J].Computer Applications,2005,25(11):2653-2657.
[6]唐发明,王仲东.一种新的二叉树多类支持向量机算法[J].计算机工程与应用,2005,41(7):24-26.TANG Fa-ming,WANG Zhong-dong.An improved multiclass support vector machines based on binary tree[J].Computer Engineering and Applications,2005,41(7):24-26.
[7]吕海波.评价饱和砂土液化新方法探讨[D].南宁:广西大学,1999.
[8]翟永杰,王东风,韩璞.基于多类支持向量机的汽轮发电机组故障诊断[J].动力工程,2003,23(5):2694-2697.ZHAI Yong-jie,WANG Dong-feng,HAN Pu.Multi-class support vector machines for fault diagnosis of turbogenerator unit[J].Power Engineering,2003,23(5):2694-2697.
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