| 基于在线LS-SVM的网络流量预测 |
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| 引用本文： | 李明迅,孟相如,温祥西,袁荣坤.基于在线LS-SVM的网络流量预测[J].电视技术,2012,36(7):67-70. |
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| 作者姓名： | 李明迅 孟相如 温祥西 袁荣坤 |
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| 作者单位： | 空军工程大学电讯工程学院,陕西西安,710077 |
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| 基金项目： | 陕西省自然科学基金项目(SJ08F14;2009JQ8008) |
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| 摘 要： | 为了提高流量预测的准确性,将混沌理论和在线LS-SVM回归技术应用于网络流量预测。采用相空间重构理论计算流量的时延τ、嵌入维数m,据此确定训练样本对并建立在线预测模型,对网络流量数据进行预测。结果表明,该方法能有效地进行流量预测,相对于传统的离线预测方式,该方法具有更好的预测精度。
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| 关 键 词： | 混沌理论 最小二乘支持向量机 网络流量 在线预测 迭代算法 |
Prediction of Network Traffic Flow Based on Online Least Squares Support Vector Machines |
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| LI Mingxun
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MENG Xiangru
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WEN Xiangxi
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YUAN Rongkun.Prediction of Network Traffic Flow Based on Online Least Squares Support Vector Machines[J].Tv Engineering,2012,36(7):67-70. |
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| Authors: | LI Mingxun MENG Xiangru WEN Xiangxi YUAN Rongkun |
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| Affiliation: | (Telecommunication Engineering Institute,Air Force Engineering University,Xi’an 710077,China) |
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| Abstract: | The chaos theory and online LS-SVM regression are applied to network traffic flow prediction to improve its accuracy.The phase space reconstruction theory is introduced to calculate the delay time(τ) and embedded dimension(m).On this basis,the training samples are formed and the online LS-SVM prediction model is constructed to predict the network traffic flow.The results show that the online LS-SVM predition model can effectively predict the network traffic flow and get higher accuracy compared with the off-line prediction model. |
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| Keywords: | chaos theory least squares support vector machines network traffic flow online prediction iterative algorithm |
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