摘要
为了获得更加理想的网络流量预测结果,准确刻画网络流量的变化趋势,提出一种基于布谷鸟搜索算法优化组合核相关向量机的网络流量预测模型(CS-HRVM)。首先针对网络流量的混沌特性,采用相空间理论建立网络流量的多维学习样本,并采用组合核函数构建相关向量机,然后将学习样本输入到相关向量机中进行训练,并采用布谷鸟搜索算法对模型参数进行优化,从而建立网络流量预测模型,最后采用仿真实验对模型性能进行仿真对比实验。结果表明,CS-HRVM获得了比其他网络流量预模型更高的预测精度,而且可以对含噪网络流量进行准确预测。
In order to obtain good forecasting results and describe the change rules network flow, a novel network flow forecasting model is proposed based on Hybrid kernels Relevance Vector Machine and Cuckoo Search algorithm（CS-HRVM）. Firstly, the learning samples are obtained by using phase reconstruction theory, and the hybrid kernels function is used to establish the relevance vector machine, and then the learning samples are input into relevance vector machine to train, and the cuckoo searching algorithm is used to optimize the parameters of model and build the model of network flow, finally, the simulation experiments are carried out to test the performance of the model. The results show that CS-HRVM has obtained higher forecasting accuracy compared with other network flow forecasting model, and can forecast accurately network flow which includes noise.
出处
《计算机工程与应用》
CSCD
2014年第17期90-94,共5页
Computer Engineering and Applications
基金
浙江省教育科学规划研究课题(No.2014SCG344)
关键词
网络流量
相空间重构
相关向量机
组合核函数
布谷鸟算法
network flow
phase space reconstruction
relevance vector machine
hybrid kernel function
cuckoo search algorithm
