文献类型：期刊文献

英文题名：Modeling and prediction of network traffic based on hybrid covariance function gaussian regressive

作者：Tian, Liang[1]; Wang, Weifeng[1]

第一作者：田亮

通讯作者：Tian, Liang

机构：[1] Department of Computer and Information Engineering, Xinxiang University, Xinxiang, China

第一机构：新乡学院计算机与信息工程学院

年份：2015

卷号：12

期号：9

起止页码：3637-3646

外文期刊名：Journal of Information and Computational Science

收录：EI(收录号：20152801011059);Scopus(收录号：2-s2.0-84935474276)

语种：英文

外文关键词：Forecasting - Gaussian distribution - Phase space methods

摘要：In order to obtain better predict results of the network traffic, this paper proposes a novel network traffic prediction model based on hybrid covariance function Gauss Process (GP). Firstly, GP model is built by using hybrid covariance function, and then the network training set is input to GP model for training to find the optimal parameter of covariance and mean function, finally, network traffic prediction model is established, and one-step and multi-step network traffic prediction test are carried out to test the performance compared with support vector machine, the neural network, and the traditional Gauss process. The results show that, compared with the contrast model, the proposed mode can describe the change trends of network traffic, and improve the prediction accuracy of network traffic, so it is an effective prediction method for complex network traffic. ?, 2015, Journal of Information and Computational Science. All right reserved.