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

作　　者：黄芳[1] 刘元君[1] 陈波[2] 

机构地区：[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

出　　处：《计算机工程与应用》2015年第19期93-97,共5页Computer Engineering and Applications

摘　　要：针对网络流量的非线性和时变性等特点,为了提高网络流量预测精度,提出一种组合核函数高斯过程的网络流量预测模型。用自相关法和假近邻法计算网络流量的延迟时间和嵌入维数,构建网络流量学习样本;采用组合核函数高斯过程对训练集进行学习,并且参数通过遗传算法进行优化;最后采用网络流量数据对模型性能测试。仿真表明,相对于对比模型,组合核函数高斯模型获得了更高的预测精度,预测结果更加稳定、可靠,具有较大的实际应用价值。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 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]