摘要
为了提高网络流量的预测精度,提出了一种小波核函数支持向量机的网络流量预测模型。首先收集网络流量历史数据,然后划分训练样本和测试样本,将训练样本输入到小波核函数支持向量机进行学习,最后采用测试样本进行仿真实验。实验表明,本文模型加快了网络流量建模的速度,提高了网络流量的预测效率,而且可以获得较高的预测精度,比传统模型具有一定的优势,具有广泛的应用前景。
In order to improve the prediction accuracy of network traffic,a new network traffic prediction model based on wavelet kernel function support vector machine is proposed.First,the historical data of network traffic is collected,then the training samples and test samples are divided,and the training samples are input to the wavelet kernel function support vector machine to learn.Finally,the test samples are used to carry out simulation experiments.Experimental results show that this model accelerated the speed of network traffic modeling,improve the efficiency of the network traffic prediction,and can obtain higher prediction accuracy,has certain superiority compared to the traditional model,has a wide application prospect.
出处
《微型电脑应用》
2016年第6期62-65,共4页
Microcomputer Applications
关键词
网络流量
支持向量机
核函数
预测模型
Network Traffic
Support Vector Machine
Kernel Function
Prediction Model
