摘要
针对网络流量预测模型存在预测稳定性不好、精度较低等问题,提出一种改进布谷鸟搜索算法优化支持向量机的网络流量预测模型(GCS-SVM)。将网络流量时间序列进行重构,采用改进布谷鸟搜索算法优化支持向量机参数,使用这组最优参数建立网络流量预测模型。仿真结果表明,GCS-SVM模型对网络流量预测是有效可行的。
There are some problems, such as low precision, on existing network traffic forecast model. In accordance with these problems, this paper proposes the network traffic forecast model of Support Vector Machine （SVM）optimized by improved Cuckoo Search algorithm（GCS）. It will transform the time series of the network traffic, and then use cuckoo search optimization algorithm to optimize the parameters of Support Vector Machine. The optimum parameters can be used to establish the model of network traffic prediction, which would make the forecast more accurate. The simulation shows that, the GCS-SVM model is a suitable and effective method for forecasting Internet traffic.
出处
《计算机工程与应用》
CSCD
2013年第21期75-78,共4页
Computer Engineering and Applications
基金
国家自然科学基金(No.60903168)
广东省教育部产学研结合项目(No.2010B090400235)
关键词
网络流量预测
高斯变异
支持向量机
布谷鸟搜索算法
network traffic prediction
Gauss mutation
Support Vector Machine （SVM）
cuckoo search algorithm
