计算机工程与应用2015，Vol.51Issue(24)：73-77,5.DOI:10.3778/j.issn.1002-8331.1312-0119
基于ELM-LSSVM的网络流量预测
Network traffic prediction based on Extreme Learning Machine and Least Square Support Vec-tor Machine
陈鸿星1
作者信息
- 1. 江西师范大学 数学与信息科学学院,南昌 330022
- 折叠
摘要
Abstract
In order to improve the prediction accuracy, aiming at the defects of the over fitting in extreme learning machine, this paper proposes a novel network traffic prediction model based on Extreme Learning Machine and Least Square Support Vector Machine(ELM-LSSVM). The phase space reconstruction is used to build learning samples of network flow and then the training samples are input to ELM and are learnt in which the Least Squares Support Vector Machine are intro-duced into Extreme Learning Machine. The simulation experiment is carried out to test the performance. The results show that the proposed model has improved the prediction accuracy of network traffic and has strong practical application value.关键词
网络流量/极限学习机/最小二乘支持向量机/相空间重构Key words
network traffic/Extreme Learning Machine(ELM)/Least Square Support Vector Machine(LSSVM)/phase space reconstruction分类
信息技术与安全科学引用本文复制引用
陈鸿星..基于ELM-LSSVM的网络流量预测[J].计算机工程与应用,2015,51(24):73-77,5.
