支持向量机及其在油田生产中的应用
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摘要
阐述了支持向量机的理论研究进程、基本原理和主要算法,并与神经网络进行了对比;介绍了支持向量机在油田生产中的应用概况.结果表明,支持向量机具有神经网络所不具备的独特优点,为解决非线性问题提供了一个新思路,是人工神经网络的替代方法.
This paper introduces the theoretical research process of SVM, the basic principles, and the main algorithms, and it is compared with the neural network. The application of the SVM in oil fields is described The result illustrates that SVM has unique excellence that Neural Network does not possess. SVM offers a new way to solve the non-linear problem, and it is delieved to be the substitute for the Neural Network.
This paper introduces the theoretical research process of SVM, the basic principles, and the main algorithms, and it is compared with the neural network. The application of the SVM in oil fields is described The result illustrates that SVM has unique excellence that Neural Network does not possess. SVM offers a new way to solve the non-linear problem, and it is delieved to be the substitute for the Neural Network.
引文
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[7] LinChun Fu,WangSheng De.Fuzzysupportvectormachines[J].IEEETransactionsonNeuralNetworks,2002,13(2):464-471.
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[11] 姚凯丰,陆文凯,丁文龙.一种基于SVM特征选择的油气预测方法[J].地球物理勘探,2004,24(7):36-38.
[12] 赵 宇,王志良,刘冀伟.一种基于SVM的测井岩性预测新方法[J].微计算机信息,2004,20(6):50-51.
[13] 肖辞源,朱白文.综合多种地震信息预测油气富集区的模糊数学方法[J].石油地球物理勘探,1990,25(2):191-200.
[14] 蔡煜东,宫家文,甘骏人,等.应用人工神经网络方法预测油气[J].石油地球物理勘探,1993,28(5):634-638.
[15] 许建华,蔡 瑞.有监督SOM神经网络在油气预测中的应用[J].石油物探,1998,37(1):71-76.
[16] 李盼池,许少华.SVM及其在复杂水淹层识别中的应用[J].计算机应用,2004,24(9):147-149.
[17] 张 浩,赵相东,陆剑峰.数据挖掘在石化企业中的应用[J].计算机工程与应用,2004,30(1):208-210.
[2] MangasarianDL,MusicantDR.Successiveoverrelaxationforsupportvectormachines[J].IEEETrans.NeuralNetworks,1999,10(5):1032-1037.
[3] ScholkopfB,SmolaA,WilliamsonRC,etal.Newsupportvectoralgorithms[J].NeuralComputation,2000,12(5):1207-1245.
[4] ChangChih Chung,LinChih Jen.TrainingV supportvectorclassifiers:theoryandalgorithms[J].NeuralComputation,2001,13(9):2119-2147.
[5] ScholkopfB,PlatJC,Shawe TaylorJ,etal.Estimatingthesupportofahigh dimensionaldistribution[J].NeuralComputation,2001,13(7):1443-1471.
[6] LinKuan Ming,KaoW,SunCL,etal.RadiusmarginboundsforsupportvectormachineswiththeRBFkernel[J].NeuralComputa tion,2003,12(6):1288-1298.
[7] LinChun Fu,WangSheng De.Fuzzysupportvectormachines[J].IEEETransactionsonNeuralNetworks,2002,13(2):464-471.
[8] ChewHong Gunn,BognerRobertE,LimCheng Chew.Dualnu supportvectormachinewitherrorrateandtrainingsizebiasing[A].Proceedingsof26thIEEEICASSP[C].SaltLake:2001.1269-1272.
[9] SuykensJ,VandewalleJ.Leastsquaresupportvectormachineclassifiers[J].NeuralProcessingLetters,1999,9(3):293-300.
[10] SuykensJ,BranbanterJD,LukasL,etal.Weightedleastsquaressupportvectormachines:robustnessandspareapproximation[J].Neurocomputing,2002,48(1):85-105.
[11] 姚凯丰,陆文凯,丁文龙.一种基于SVM特征选择的油气预测方法[J].地球物理勘探,2004,24(7):36-38.
[12] 赵 宇,王志良,刘冀伟.一种基于SVM的测井岩性预测新方法[J].微计算机信息,2004,20(6):50-51.
[13] 肖辞源,朱白文.综合多种地震信息预测油气富集区的模糊数学方法[J].石油地球物理勘探,1990,25(2):191-200.
[14] 蔡煜东,宫家文,甘骏人,等.应用人工神经网络方法预测油气[J].石油地球物理勘探,1993,28(5):634-638.
[15] 许建华,蔡 瑞.有监督SOM神经网络在油气预测中的应用[J].石油物探,1998,37(1):71-76.
[16] 李盼池,许少华.SVM及其在复杂水淹层识别中的应用[J].计算机应用,2004,24(9):147-149.
[17] 张 浩,赵相东,陆剑峰.数据挖掘在石化企业中的应用[J].计算机工程与应用,2004,30(1):208-210.
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