计算机应用 ›› 2010, Vol. 30 ›› Issue (10): 2648-2652.

• 网络与通信 • 上一篇    下一篇

基于改进Elman神经网络的网络流量预测

党小超1,郝占军2   

  1. 1. 西北师范大学
    2. 兰州市西北师范大学
  • 收稿日期:2010-04-22 修回日期:2010-06-28 发布日期:2010-09-21 出版日期:2010-10-01
  • 通讯作者: 党小超
  • 基金资助:
    甘肃省科技支撑计划项目

Prediction for network traffic based on modified Elman neural network

  • Received:2010-04-22 Revised:2010-06-28 Online:2010-09-21 Published:2010-10-01

摘要: 针对网络系统非线性、多变量、时变性等特点,提出一种改进的Elman神经网络模型。在该模型的训练过程中引入了季节周期性学习方法,并对某高校主干网络出口流量进行实验检测。实验结果表明,该模型具有良好的预测效果,相对于传统线性模型、BP神经网络模型及标准Elman神经网络模型具有更高的预测精度和更好的自适应性。最后,通过自适应边界值方法进行检测,能够及时发现异常流量行为,说明该模型应用于网络流量预测是可行、有效的。

关键词: Elman神经网络, 网络流量, 建模, 预测, 网络行为

Abstract: Concerning the nonlinear, multivariable and time-varying qualities of neural network, a modified Elman neural network model was proposed. The learning method based on seasonal periodicity was introduced into the model training. And the output traffic of the backbone network of a certain university was given. The experimental results show that this model has better predication effect. Compared with the traditional linear model, the BP neural network model and the normal Elman neural network model, it has higher precision and better adaptability. Finally, abnormal behaviors of network traffic can be found on time through test of adaptive boundary value method, which proves that the model is feasible and effective.

Key words: Elman neural network, network traffic, modeling, prediction, network behavior

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