Search

Article

x

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

Nonlinear prediction of small scale network traffic based on local relevance vector machine regression model

Meng Qing-Fang Chen Yue-Hui Feng Zhi-Quan Wang Feng-Lin Chen Shan-Shan

Citation:

Nonlinear prediction of small scale network traffic based on local relevance vector machine regression model

Meng Qing-Fang, Chen Yue-Hui, Feng Zhi-Quan, Wang Feng-Lin, Chen Shan-Shan
PDF
Get Citation

(PLEASE TRANSLATE TO ENGLISH

BY GOOGLE TRANSLATE IF NEEDED.)

Metrics
  • Abstract views:  3400
  • PDF Downloads:  970
  • Cited By: 0
Publishing process
  • Received Date:  10 January 2013
  • Accepted Date:  24 April 2013
  • Published Online:  05 August 2013

Nonlinear prediction of small scale network traffic based on local relevance vector machine regression model

  • 1. School of Information Science and Engineering, University of Jinan, Jinan 250022, China; Shandong Provincial Key laboratory of Network Based Intelligent Computing, Jinan 250022
Fund Project: Project supported by the National Natural Science Foundation of China (Grant Nos. 61201428, 61070130, 61173079), the Natural Science Foundation of Shandong Province, China (Grant Nos. ZR2010FQ020, ZR2011FZ003), the Shandong Distinguished Middle-aged and Young Scientist Encourage and Reward Foundation, China (Grant No. BS2009SW003), and the China Postdoctoral Science Foundation (Grant No. 20100470081).

Abstract: Based on the nonlinear time series local prediction method and the relevance vector machine regression model, the local relevance vector machine prediction method is proposed and applied to predict the small scale traffic measurement data, and the BIC-based neighbor point selection method is used to choose the number of nearest-neighbor points for the local relevance vector machine regression model. We also compare the performance of the local relevance vector machine regression model with the feed-forward neural network optimized by particle swarm optimization for the same problem. Experimental results show that the local relevance vector machine prediction method whose neighboring points have been optimized can effectively predict the small scale traffic measurement data, can reproduce the statistical features of real small scale traffic measurements, and the prediction accuracy of the local relevance vector machine regression model is superior to that of the feedforward neural network optimized by PSO and the local linear prediction method.

Reference (32)

Catalog

    /

    返回文章
    返回