现代电子技术2011，Vol.34Issue(3)：65-67,71,4.
基于混沌时间序列和神经网络的网络流量预测方法
Approach of Network Flow Prediction Based on Chaotic Time Series and Neural Network
摘要
Abstract
For the network flow time series has complex non-linear and uncertainty characters, a approach of network flow prediction was presented according to the phase space reconstruction theory (PSRT) combined with recurrent neural network ( RNN).The optimal delay tmie and minimal embedding dimension are determined by PSRT, and then the network traffic time series is reconstructed.The reconstructed time series is trained by RNN to obtain a suitable model,which is applied to the prediction of network flow in the network nodes.The new method applied to the prediction of the actual data is more accurate and stable in comparison with the method of traditional time series prediction.The results show that the new method is effective and practical in the field of the actual time-sequence prediction.关键词
时间序列/相空间重构/神经网络/网络流量预测分类
信息技术与安全科学引用本文复制引用
奠石镁,何蓉,付绍武..基于混沌时间序列和神经网络的网络流量预测方法[J].现代电子技术,2011,34(3):65-67,71,4.
