物理学报Issue(15)：1-7,7.DOI:10.7498/aps.62.150509
基于局域相关向量机回归模型的小尺度网络流量的非线性预测*
Nonlinear prediction of small scale network traffic based on local relevance vector machine regression model*
摘要
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 neigh-bor 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.关键词
小尺度网络流量/非线性时间序列预测方法/局域预测法/相关向量机回归模型Key words
small scale network traffic/nonlinear time series prediction method/local prediction method/relevance vector machine regression model引用本文复制引用
孟庆芳,陈月辉,冯志全,王枫林,陈珊珊..基于局域相关向量机回归模型的小尺度网络流量的非线性预测*[J].物理学报,2013,(15):1-7,7.基金项目
国家自然科学基金(批准号:61201428,61070130,61173079)、山东省自然科学基金(批准号：ZR2010FQ020, ZR2011FZ003)、山东省优秀中青年科学家科研奖励基金(批准号：BS2009SW003)和中国博士后科学基金(批准号:20100470081)资助的课题.* 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) （批准号:61201428,61070130,61173079）
