DocumentCode
1949173
Title
Notice of Retraction
Study on network flow prediction model based on particle swarm optimization algorithm and RBF neural network
Author
Zhang Yu Bin ; Lin Li Zhong ; Zhang Ya Ming
Author_Institution
HeBei Vocational Art Coll., Shijiazhuang, China
Volume
2
fYear
2010
fDate
9-11 July 2010
Firstpage
302
Lastpage
306
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
It is significant to control network congestion by time series forecasting research for network flow. The hybrid method of particle swarm optimization algorithm and RBF neural network is applied to predict network flow and gain the desirable network flow prediction results. In the hybrid method, particle swarm optimization algorithm is selected and adjusted to the connection weights and the center of radial basis function and the width of radial basis function. The network flow data are collected to search the prediction ability of particle swarm optimization algorithm and RBF neural network. Compared with the results of RBF neural network and BP neural network, particle swarm optimization algorithm and RBF neural network has better forecasting performance.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
It is significant to control network congestion by time series forecasting research for network flow. The hybrid method of particle swarm optimization algorithm and RBF neural network is applied to predict network flow and gain the desirable network flow prediction results. In the hybrid method, particle swarm optimization algorithm is selected and adjusted to the connection weights and the center of radial basis function and the width of radial basis function. The network flow data are collected to search the prediction ability of particle swarm optimization algorithm and RBF neural network. Compared with the results of RBF neural network and BP neural network, particle swarm optimization algorithm and RBF neural network has better forecasting performance.
Keywords
backpropagation; forecasting theory; particle swarm optimisation; radial basis function networks; telecommunication congestion control; time series; BP neural network; RBF neural network; network congestion control; network flow data; network flow prediction; particle swarm optimization; radial basis function; time series forecasting; Birds; Equations; Particle swarm optimization; network flow; neural network; particle swarm optimization algorithm; prediction model;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4244-5537-9
Type
conf
DOI
10.1109/ICCSIT.2010.5564575
Filename
5564575
Link To Document