| 边坡位移非线性时间序列采用支持向量机算法的智能建模与预测研究 |
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| 引用本文： | 刘开云,乔春生,滕文彦. 边坡位移非线性时间序列采用支持向量机算法的智能建模与预测研究[J]. 岩土工程学报, 2004, 26(1): 57-61 |
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| 作者姓名： | 刘开云 乔春生 滕文彦 |
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| 作者单位： | 北京交通大学土木建筑工程学院；石家庄铁路工程职业技术学院土木工程系 北京 100044；北京 100044；河北石家庄 050041； |
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| 基金项目： | 国家自然科学基金资助项目(50078002) |
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| 摘 要： | 介绍了人工智能领域最新的基于结构风险最小化原理的数据挖掘算法---支持向量机算法,运用Matlab语言编写了程序,采用不同的核函数对具体的边坡工程实例作了计算,并将人工神经元网络计算结果与之对比,可见无论是在学习或预测精度方面,支持向量机算法较基于经验风险最小化原理的人工神经元网络算法都有很大的优越性,可以运用于实际工程。
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| 关 键 词： | 数据挖掘 支持向量机 回归算法 机器学习 位移预测 |
| 文章编号： | 1000-4548(2004)01-0057-05 |
Research on non-linear time sequence intelligent model construction and prediction of slope displacement by using support vector machine algorithm |
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| LIU Kaiyun,QIAO Chunsheng,TENG Wenyan. Research on non-linear time sequence intelligent model construction and prediction of slope displacement by using support vector machine algorithm[J]. Chinese Journal of Geotechnical Engineering, 2004, 26(1): 57-61 |
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| Authors: | LIU Kaiyun QIAO Chunsheng TENG Wenyan |
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| Affiliation: | 1.School of Civil Engineering and Architecture Beijing Jiaotong University Beijing 100044 China 2.Department of Civil Engineering Shijiazhuang Institute of Railway Engineering Shijiazhuang 050041 China |
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| Abstract: | Based on the Structural Risk Minimization principle,the latest data mining method in artificial intelligence field—support vector machine algorithm was introduced in this paper.A program was worked out in language Matlab for a slope engineering project by using different kernel function.Compared with the result obtained by using the Artificial Neural Network algorithm based on the Empirical Risk Minimization principle,the SVM algorithm is obviously superior to the ANN algorithm whatever on machine learning or prediction accuracy and it can be used to practical engineering. |
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| Keywords: | data mining support vector machine regression algorithm machine learning displacement prediction |
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