计算机技术与发展2016，Vol.26Issue(6)：73-77,5.DOI:10.3969/j.issn.1673-629X.2016.06.016
基于优化组合核极限学习机的网络流量预测
Network Flow Prediction Based on Optimization Combined Kernel Extreme Learning Machine
摘要
Abstract
In order to improve precision of network flow prediction,a prediction model is proposed in this paper based on Empirical Mode Decomposition ( EMD) and chaos particle swarm optimization combined kernel extreme learning machine aiming at the features of non-linear and non-stationary for network flow data. Unit flow is obtained through EMD on the network flow in time sequence,then each unit data is predicted with kernel extreme learning machine. Finally,the prediction result is reconstructed. In view of the inadequate fitting ca-pacity of traditional kernel extreme learning machine,a machine combining Gaussian kernel and multinomial kernel is proposed and the improved kernel parameter combination and penalty factor of chaos particle swarm optimization with combined kernel are applied in the prediction of network flow. The experiment shows that this method can improve the accuracy of network prediction effectively,and help guide the rational allocation and planning of network resources.关键词
网络流量预测/核极限学习机/组合核函数/混沌粒子群/经验模态分解Key words
network flow prediction/kernel extreme learning machine/combined kernel function/chaos particle swarm/empirical mode decomposition分类
信息技术与安全科学引用本文复制引用
刘悦,王芳..基于优化组合核极限学习机的网络流量预测[J].计算机技术与发展,2016,26(6):73-77,5.基金项目
河南省教育科学技术研究重点项目(15C520016) （15C520016）
开封市科技攻关计划项目(130145) （130145）
