Open AccessNonlinear prediction of small scale network traffic based on local relevance vector machine regression model
Author(s) -
Qingchun Meng,
Yuehui Chen,
Zhiquan Feng,
FengLin Wang,
Shanshan Chen
Publication year - 2013
Publication title -
wuli xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.199
H-Index - 47
ISSN - 1000-3290
DOI - 10.7498/aps.62.150509
Subject(s) - relevance vector machine , support vector machine , computer science , artificial neural network , relevance (law) , particle swarm optimization , artificial intelligence , feedforward neural network , regression , machine learning , data mining , scale (ratio) , algorithm , mathematics , statistics , physics , quantum mechanics , political science , law
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.