计算机科学 ›› 2009, Vol. 36 ›› Issue (7): 244-246.doi: 10.11896/j.issn.1002-137X.2009.07.060

• 人工智能 • 上一篇    下一篇

混沌一支持向量机回归在流量预测中的应用研究

罗赟骞,夏靖波,王焕彬   

  1. (空军工程大学电讯工程学院 西安710077);(西安电子科技大学综合业务网理论及关键技术国家重点实验室 西安710071)
  • 出版日期:2018-11-16 发布日期:2018-11-16
  • 基金资助:
    本文受综合业务网理论及关键技术重点实验室开放基金(ISN-9-08)资助。

Application of Chaos-support Vector Machine Regression in Traffic Prediction

LUO Yun-qian,XIA Jing-bo,WANG Huan-bin   

  • Online:2018-11-16 Published:2018-11-16

摘要: 为了提高流量预测准确性,将混沌理论和支持向量机回归应用于网络流量预测。采用相空间重构理论计算实际流量的延时、嵌入维数和Lyapunov指数,证实网络流量存在混沌现象;据此建立混沌一支持向量机预测模型并确定训练样本对,对实际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BP神经网络方法,该方法具有更好的预测精度。

关键词: 支持向量机,流量预测,回归,混沌

Abstract: A traffic forecasting model based on the support vector machine(SVM) and chaos was developed to improve the accuracy of the traffic prediction. Based on the phase space reconstruction,it calculates the real-time traffic's delay time, embedded dimension and I_yapunov exponent, and proves that the traffic chaos phenomena exists. hhat a chaos-SVM model was constructed and pairs of training samples was determined to forecast the real network traffic. The resups show that the chaos-SVM model is able to predict network traffic effectively. In comparison with the BP neutral network, it has higher accuracy of prediction.

Key words: Support vector machine(SVM) , Traffic prediction, Regression, Chaos

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