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支持向量机及其在油田生产中的应用

Supportive vector machine and its application in oil fields

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【Author】 LI Zhuo~1, LIU Bin~2, LIU Tie-nan~2, ZHU Xiu-hua~3, WEI Kun~4 ( 1. Geoscience College, Daqing Petroleum Institute, Daqing, Heilongjiang 163318, China; 2. Electric and Information Engineering College, Daqing Petroleum Institute, Daqing, Heilongjiang 163318, China; 3. Adult Education School, Daqing Petroleum Institute, Daqing, Heilongjiang 163318, China; 4. Physics and Mathematics School, Harbin Engineering University, Harbin, Heilongjiang 150001, China )

【机构】大庆石油学院地球科学学院；大庆石油学院电气信息工程学院；大庆石油学院成人教育学院；哈尔滨工程大学理学院黑龙江大庆163318；黑龙江大庆163318；黑龙江哈尔滨150001；

【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.更多还原

【Abstract】 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.更多还原

【关键词】统计学习理论；支持向量机；神经网络；
【Key words】 statistical learning theory； SVM； Neural Network；

【文献出处】大庆石油学院学报 ,Journal of Daqing Petroleum Institute , 编辑部邮箱 ,2005年03期

【分类号】TP181
【被引频次】11
【下载频次】159

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