基于混沌理论和神经网络的网络流量预测

Network Flow Forecasting based on Chaos Theory and Neural Network Model

针对目前网络流量预测不能很好地满足智能网络管理需求的现状,分析网络流量数据内在混沌特性,主要包括时间延迟、嵌入维数、关联维数及Lyapunov指数的计算,并将此分析耦合人工神经网络模型进行预测,最后给出某网络中心流量预测的实例,结果显示基于混沌时间序列分析的神经网络流量预测在数据动力特征刻画及误差控制上有显著优势。

Flow prediction s for the current network can not be well positioned to meet the demand for intelligent network management,a new forecasting model based on Chaos theory and Neural Network is developed,this paper has analyzed the Chaos property of Network flow and calculated time delay,embedding dimension and Lyapunov exponent. Finally,the paper made a Network flow prediction of a network-centric with a competitively better result especially in the aspect of tracing dynamic character and error control.