Network traffic prediction based on improved BP wavelet neural network

Peng Wang, Yuan Liu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

20 Scopus citations

Abstract

Considering that traditional BP wavelet neural network (BPWNN) is easy to take local convergence and has slowly learning convergent velocity. We apply a method based on adaptive learning rate to optimize it in accelerating the learning convergent velocity. In prediction, firstly, denoised the traffic time series with wavelet packet transform to improve the prediction precision, then compared the ability of BP neural network (BPNN) and improved BPWNN (IBPWNN)to the prediction of network traffic. The emulation experiment results indicate that in the case of one-step prediction, BPNN and IBPWNN have similar prediction precision, however, in the case of multi-step prediction; the BPNN has low prediction precision, while the IBPWNN still performs a good ability to prediction.

Original languageEnglish
Title of host publication2008 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2008
DOIs
StatePublished - 2008
Externally publishedYes
Event2008 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2008 - Dalian, China
Duration: 12 Oct 200814 Oct 2008

Publication series

Name2008 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2008

Conference

Conference2008 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2008
Country/TerritoryChina
CityDalian
Period12/10/0814/10/08

Keywords

  • Network traffic predictio n
  • Wavelet neural network
  • Wavelet packet
  • Wavelet packet De-noising

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