Network flow prediction based on grey-support vector regression technology

Author

Yuan, Li ; Hua, Liu ; Zhi-Guo, Liu

Author_Institution

ShiJiaZhuang Coll., Shijiazhuang, China

Volume

3

fYear

2010

fDate

22-24 June 2010

Abstract

In order to solve the shortcoming of BP neural network, a novel prediction method is presented to predict network flow. The combination of grey prediction model and support vector regression is applied to predict network flow in the paper. We employ collected network flow experimental data to test the performance of the combination model of grey prediction model and support vector regression. The mean relative error of grey prediction model and support vector regression is 1.94, while the mean relative error of BP neural network is 3.43. It can be seen that the combination model of grey prediction model and support vector regression is superior to BP neural network.

Keywords

backpropagation; computer networks; grey systems; regression analysis; support vector machines; telecommunication computing; BP neural network; grey prediction model; grey-support vector regression technology; mean relative error; network flow prediction; prediction method; Computer networks; Computer science education; Educational institutions; Educational technology; Neural networks; Prediction methods; Predictive models; Support vector machines; Technology forecasting; Testing; forecasting technology; grey; network flow; support vector regression;

fLanguage

English

Publisher

ieee

Conference_Titel

Education Technology and Computer (ICETC), 2010 2nd International Conference on

Conference_Location

Shanghai

Print_ISBN

978-1-4244-6367-1

Type

conf

DOI

10.1109/ICETC.2010.5529536

Filename

5529536