小波消噪和蚁群优化支持向量机的网络流量预测模型  

Prediction model of the network traffic based on wavelet de-noising and support vector machine optimized by ant colony optimization algorithm

作　　者：陆莉芳[1] 

机构地区：[1]曲靖师范学院,云南曲靖655011

出　　处：《激光杂志》2014年第11期87-90,共4页Laser Journal

摘　　要：针对当前网络流量预测模型精度低的缺点,本文提出了一种新型的小波消噪和蚁群算法优化支持向量机的网络流量预测模型。首先采用小波阈值法对网络流量进行消噪处理;然后将网络流量输入到支持向量机中学习,并采用蚁群算法对支持向量机的参数进行优化,建立网络流量预测模型,最后采用实际网络流量数据进行仿真实验,结果表明,相对于其它网络流量预测模型,本文模型提高了网络流量的预测精度,具有更好的鲁棒性。In order to solve the low prediction accuracy of network traffic prediction model,a new model of network traffic based on wavelet de-noising and support vector machine optimized by ant colony optimization algorithm is proposed in this paper.Firstly,the network traffic is processed by wavelet de-noising method,secondly,the data are input to support vector machine to learn which colony algorithm is used to optimize parameters of support vector machine to establish the prediction model of network traffic,finally,the performance is test by network traffic data.The simulation results achieved with the actual traffic flow data show that the accuracy and the adaptability in the prediction are improved significantly which verifies that the proposed method is effective for network traffic prediction.

关 键 词：网络流量预测 小波消噪 支持向量机 蚁群优化算法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]