| 基于支持向量机的石油需求预测 |
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| 引用本文： | 陈涛,杨凯凡,杨晓.基于支持向量机的石油需求预测[J].河南科学,2008,26(12). |
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| 作者姓名： | 陈涛 杨凯凡 杨晓 |
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| 作者单位： | 陕西理工学院,数学系,陕西,汉中,723000 |
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| 基金项目： | 国家自然科学皋金项目资助 |
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| 摘 要： | 支持向量机是基于结构风险最小化原理的一种学习技术,是一种具有很好泛化能力的预测工具,它有效地解决小样本、非线性、高维数、局部极小等问题.利用支持向量回归机对我国石油需求量进行预测,并通过实验与神经网络的预测结果进行比较,表明支持向量机具有更高的预测精度.
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| 关 键 词： | 支持向量机 结构风险最小化 神经网络 石油需求 |
Oil Consumption Forecast Based on Support Vector Machine |
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| Chen Tao,Yang Kaifan,Yang Xiao.Oil Consumption Forecast Based on Support Vector Machine[J].Henan Science,2008,26(12). |
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| Authors: | Chen Tao Yang Kaifan Yang Xiao |
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| Institution: | Chen Tao,Yang Kaifan,Yang Xiao(Department of Mathematics,Shaanxi University of Technology,Hanzhong 723000,Shaanxi China) |
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| Abstract: | Support Vector Machine is a learning technology based on structure risk minimization and a predictive tool with better generalization ability,and it effectively solute the fewer samples,nonlinear,high dimension and local minima. This paper predict oil consumption by support vector machine. And it compare the results by neural networks through experiment. Experiment shows that SVM is higher forecast accuracy. |
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| Keywords: | Support Vector Machin(eSVM) structure risk minimization artificial neural network oil consumption |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
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