现代电子技术2016，Vol.39Issue(18)：73-75,79,4.DOI:10.16652/j.issn.1004-373x.2016.18.018
基于人工鱼群算法的网络流量预测方法
Network traffic forecasting method based on artificial fish swarm algorithm
摘要
Abstract
The support vector regression algorithm of the nonlinear prediction model is used in this paper to establish the forecasting model to study the prediction of the network traffic with obvious nonstationarity,chaos and nonlinearity. In this pa⁃per,the artificial fish swarm algorithm is adopted to optimize the parameters of support vector regression algorithm. The PSO al⁃gorithm is used to improve the conventional artificial fish swarm algorithm. The Logistic map is used to initialize the position of the artificial fish to improve the diversity of the population,so as to improve the global optimization ability of the algorithm and avoid the algorithm falling into local minimum value. The data of three groups with different time granularity are analyzed by using MAWI data. The results show that the artificial fish swarm algorithm has better prediction performance and can meet the needs of network traffic prediction.关键词
网络流量预测/人工鱼群算法/支持向量回归/混沌机制/粒子群优化Key words
network traffic prediction/artificial fish swarm algorithm/support vector regression/chaos mechanism/parti-cle swarm optimization分类
信息技术与安全科学引用本文复制引用
马浩..基于人工鱼群算法的网络流量预测方法[J].现代电子技术,2016,39(18):73-75,79,4.基金项目
国家自然科学基金项目（61303232）；2015年广东省佛山市机电专业群工程技术开发中心开放基金基于物联网技术的产品质量控制系统设计与开发 （）
