计算机工程与应用Issue(19)：93-97,5.DOI:10.3778/j.issn.1002-8331.1309-0533
组合核函数高斯过程的网络流量预测模型
Prediction model of network traffic based on combined kernel function ;Gaussian regression
黄芳 1刘元君 1陈波2
作者信息
- 1. 湖南商务职业技术学院 电子信息技术系，长沙 410205
- 2. 电子科技大学 计算机科学与工程学院，成都 611731
- 折叠
摘要
Abstract
In order to improve the prediction precision of network traffic, this paper proposes a network traffic prediction model based on combined kernel function Gauss Process(GP)to describe the nonlinear and time-varying characteristics of network traffic. Firstly, the time delay and embedding dimension of network traffic are calculated by self correlation method and false nearest neighbor method, and training samples of network traffic are generated, and then the training set is input to combination kernel function GP learning to establish a network traffic prediction model which the genetic algo-rithm is used to find the optimal parameters of GP, and finally, the simulation experiments is carried out on network traffic data. The results show that, compared with the other models, the proposed model can obtain higher prediction precision of network traffic, the prediction results are more stable and reliable, so it has great practical application value.关键词
高斯过程/遗传算法/延迟时间/网络流量/嵌入维数Key words
Gaussian Process(GP)/genetic algorithm/delay time/network traffic/embedding dimension分类
信息技术与安全科学引用本文复制引用
黄芳,刘元君,陈波..组合核函数高斯过程的网络流量预测模型[J].计算机工程与应用,2015,(19):93-97,5.
