DocumentCode
1949036
Title
Notice of Retraction
Application of genetic algorithm and RBF neural network in network flow prediction
Author
Zhang Ya Ming ; Zhang Yu Bin ; Lin Li Zhong
Author_Institution
ShiJiaZhuang Inf. Eng. Vocational Coll., Shijiazhuang, China
Volume
2
fYear
2010
fDate
9-11 July 2010
Firstpage
298
Lastpage
301
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.
Time series forecasting is the main method in network flow prediction. RBF neural network is capable of universal approximation, which not only has fast training velocity, but also can solve the local minima problem. Thus, network flow prediction technology based on genetic algorithm and RBF neural network is presented in the paper. And the training parameters are adjusted by genetic algorithm. Network flow data about 40 points can be applied to study the superiority of genetic algorithm and RBF neural network neural network compared with normal RBF neural network. By the analysis of application case, it can be seen that the forecasting performance of genetic algorithm and RBF neural network is better than that of normal RBF neural network.
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.
Time series forecasting is the main method in network flow prediction. RBF neural network is capable of universal approximation, which not only has fast training velocity, but also can solve the local minima problem. Thus, network flow prediction technology based on genetic algorithm and RBF neural network is presented in the paper. And the training parameters are adjusted by genetic algorithm. Network flow data about 40 points can be applied to study the superiority of genetic algorithm and RBF neural network neural network compared with normal RBF neural network. By the analysis of application case, it can be seen that the forecasting performance of genetic algorithm and RBF neural network is better than that of normal RBF neural network.
Keywords
approximation theory; forecasting theory; genetic algorithms; radial basis function networks; time series; RBF neural network; genetic algorithm; network flow prediction; time series forecasting; universal approximation; Forecasting; Genetics; Knowledge based systems; Neural networks; Prediction algorithms; RBF neural network; genetic algorithm; network flow; time series prediction;
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.2010.5564566
Filename
5564566
Link To Document