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基于过程支持向量回归机的油田开发指标预测模型

A Model of Oilfield Development Index Forecast Base on Process Support Vector Regression Machine
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摘要 针对传统支持向量回归机在机制上难以直接对时变信号进行处理,提出了一种用于时间序列预测的过程支持向量回归模型,面向油田开发指标综合分析预测等问题,提出了一种过程支持向量回归机模型,建立了基于涡流搜索的优化学习算法,方法可综合历史数据和开发条件,实现对油田开发指标的预测。 Aiming at the traditional support vector regression machine on the mechanism can't to solute dynamic time-varying signal pattern classification, proposes a process support vector regression time series prediction model. For the comprehensive analysis and prediction of oilfield development indexes, proposed a process support vector regression model. The learning algorithm based on vortex search algorithm is established, it can be used to realize the forecast of oil field development index with the comprehensive historical data and development conditions.
作者 赵玲 李学贵 许少华 夏惠芬 ZHAO Ling1, LI Xue-gui1, XU Shao-hua2, XIA Wui-fen3 (1. School of Computer & Information, Northeast Petroleum University, Daqing 163318, China;2. College of Information Science and Technology, Shandong University of Science and Technology, Qingdao 266590, China ;3. School of Petroleum Engineering, Northeast Petroleum University, Daqing 163318, China)
出处 《数学的实践与认识》 北大核心 2018年第10期83-88,共6页 Mathematics in Practice and Theory
基金 国家自然科学基金(61402099) 2017东北石油大学引导性创新基金(2017YDL-10,2016YDL-13)
关键词 过程支持向量机 涡流搜索 开发指标预测 process support vector machine vortex search development index forecast
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  • 1范玉刚,李平,宋执环.基于样本取样的SMO算法[J].信息与控制,2004,33(6):665-669. 被引量:5
  • 2王国胜.核函数的性质及其构造方法[J].计算机科学,2006,33(6):172-174. 被引量:38
  • 3Vapnik V N. Statistical learning theory [M]. New York: Wildy, 1998. 被引量:1
  • 4Felipe Cucker, Steve Smale. On the mathematical foundations of learning [J]. Bulletin of the Ameriean Mathematical Society, 2001, 39(1): 1-49. 被引量:1
  • 5Scholkopf, Sung K, Burges C, et al. Comparing support vector machines with Gaussian kernels to radial basis function classifiers [J]. IEEE Trans on Signal Processing, 1997, 45(11): 2758-2765. 被引量:1
  • 6Collobert R , Bengio S. SVMTorch : A support vector machine for large scale regression and classification problems[J]. J of Machine Learning Research, 2001, 1 (2) : 143-160. 被引量:1
  • 7VAPNIK V N. Statistical learning theory[M]. New York: Wildy, 1998. 被引量:1
  • 8FELIPE C, STEVE S. On the mathematical foundations of learning[J]. Bulletin of the American Mathematical Society, 2001,39 (1) : 1--49. 被引量:1
  • 9HAZEM M, El-Bakry, NIKOS M. A new approach for fast face detection[J]. WSEAS Transactions on Information Science and Applications, 2006,3(9) : 1 725-- 1 730. 被引量:1
  • 10HE Xingui, LIANG Jiuzhen. Procedure neural networks[R]. Proceedings of conference on intelligent information proceeding, 16th World Computer Congress 2000. Beijing: Publishing House of Electronic Industry, 2000:143--146. 被引量:1

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