摘要
研究网络流量预测问题,网络流量具有突发性、周期性、非线性特点,传统网络流量预测模型无法建立准确预测模型,导致预测误差大,预测精度低.为了提高网络流量的预测精度,提出一种小波分解和支持向量机的网络流量预测模型.首先采用小波变换对网络流量进行分解,把网络流量不同特性成分分离出来,然后采用支持向量机对各分量进行预测,最后采用小波变换对各分量预测结果进行重构,得到网络流量的最终预测结果.仿真实验结果表明,相对其它预测模型,提高了网络流量的预测精度,为网络流量预测优化提供了可靠依据.
Study of network traffic prediction, network traffic has a sudden, periodic, nonlinear characteristics, the traditional network traffic prediction model unable to establish accurate prediction model, lead to the prediction error, low accuracy of prediction. In order to improve the prediction accuracy of network traffic, put forward a kind of wavelet decomposition and support vector machine network traffic prediction model. Wavelet transform is adopted to network decomposition, the network traffic characteristics of components separated, then by using support vector machine to each component are predicted, the wavelet transform is adopted to predict the results of reconstruction of the components, network traffic has been the final forecasting result. The simulation results show that, compared with other prediction models, improve the prediction accuracy of network traffic, for the prediction of network traffic flow optimization provides reliable basis.
出处
《微电子学与计算机》
CSCD
北大核心
2012年第9期193-196,200,共5页
Microelectronics & Computer
关键词
网络流量
小波分解
支持向量机
粒子群算法
network traffic
wavelet decomposition
support vector machine
PSO
