计算机工程与应用2012，Vol.48Issue(21)：83-88,6.DOI:10.3778/j.issn.1002-8331.2012.21.018
小波消噪和神经网络的网络流量混沌预测
Chaotic prediction for network traffic flow based on wavelet de-noising and neural network
高述涛1
作者信息
- 1. 湖南外贸职业学院服务外包学院,长沙410014
- 折叠
摘要
Abstract
The network traffic data contain a lot of noise, and they have negative effect on the network traffic prediction accuracy, therefore, this paper proposes network flow prediction model based on wavelet de-noising and neural network. The network traffic data are de-noised by wavelet. The input number of BP neural network is determined by correlation dimension. The BP neural network is used to establish the prediction model of network traffic flow. The results show that, compared with the model which doesn't carry out de-noising, the proposed model can more accurately describe the change of the network traffic trends, so as to effectively improve the prediction accuracy of network traffic. It provides a new research idea for the nonlinear prediction problem.关键词
小波消噪/神经网络/网络流量/相空间重构Key words
wavelet de-noising/ neural network/ network traffic flow/ phase space reconstruction分类
信息技术与安全科学引用本文复制引用
高述涛..小波消噪和神经网络的网络流量混沌预测[J].计算机工程与应用,2012,48(21):83-88,6.
