互联网 qkzz.net
全刊杂志网:首页 > 女性 > 文章正文
刊社推荐

融合小波变换与贝叶斯LS-SVM的网络流量预测


□ 刘 渊 王 鹏

  摘 要:为了提高网络流量预测的精度,研究了一种融合小波变换与贝叶斯LS-SVM的网络流量预测方法。首先将原始流量数据时间序列进行小波分解,并将分解得到的近似部分和各细节部分分别单支重构到原级别上;对各个重构后的序列分别用最小二乘支持向量机进行预测,将贝叶斯证据框架应用于最小二乘支持向量机模型参数的选择;将各个预测结果重构后得到对原始序列的预测结果。对比实验表明,该模型不仅具有较快的运行速度,而且具有较高的预测精度。
  关键词:网络流量预测;小波变换;支持向量机;最小二乘支持向量机;贝叶斯框架
  中图分类号:TP393.01文献标志码:A
  文章编号:1001-3695(2009)06-2229-03
  doi:10.3969/j.issn.1001-3695.2009.06.069
  
  Combining wavelet transform and Bayesian least squares supportvector machines to predict network traffic
  LIU Yuana,b,WANG Penga
  (a.School of Information Engineering,b.Digital Media Research Center, Jiangnan University, Wuxi Jiangsu 214122, China)
  Abstract:In order to improve the precision of the network traffic prediction,this paper proposed a new method of network traffic prediction combining wavelet transform and least squares support vector machines (LS-SVM).The original network traffic time series was decomposed into approximate series and several detail series. The result of single branch reconstruction of each decomposed series was more unitary than the original series in frequency, and it could be built traffic model with LS-SVM.The Bayesian evidence framework was applied to LS-SVM in order to determine the regularization parameters and kernel parameters effectively. The prediction of the original series could be obtained by the synthesis of each reconstructed series’ prediction result. The simulation results indicate the high accuracy and speed of this method. ......
很抱歉,暂无全文,若需要阅读全文或喜欢本刊物请联系《计算机应用研究》杂志社购买。
欢迎作者提供全文,请点击编辑
分享:
 

了解更多资讯,请关注“木兰百花园”
分享:
 
精彩图文


关键字
支持中国杂志产业发展,请购买、订阅纸质杂志,欢迎杂志社提供过刊、样刊及电子版。
关于我们 | 网站声明 | 刊社管理 | 网站地图 | 联系方式 | 中图分类法 | RSS 2.0订阅 | IP查询
全刊杂志赏析网 2017