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.
出处
《计算机工程与应用》
CSCD
北大核心
2015年第19期93-97,共5页
Computer Engineering and Applications
关键词
高斯过程
遗传算法
延迟时间
网络流量
嵌入维数
Gaussian Process（GP）
genetic algorithm
delay time
network traffic
embedding dimension
