微型电脑应用2017，Vol.33Issue(1)：67-70,4.
和声搜索算法优化支持向量机的网络流量预测
Network Traffic Predicting Based on SVM Optimized by Harmony Search Algorithm
摘要
Abstract
Network flow is influenced by some external factors,and has complicated variation law.In order to improve prediction effect of network traffic,this paper puts forward a novel network traffic prediction model based on HS-SVM.First of all,current research status of network traffic prediction is analyzed deeply,and chaotic characteristics of network traffic are pointed out;Then,delay time and embedding dimension are determined by chaos theory to reconstruct original network traffic data;Finally,network traffic prediction model is established by HS-SVM and the simulation test is carried out compared with other network traffic prediction models.HS-SVM can mine and analyze the change law of network traffic,prediction results are better than that of other prediction models,and the test results verify feasibility and superiority of HS-SVM.关键词
互联网/网络流量/混沌理论/和声搜索算法/参数选择Key words
The Internet/Network traffic/Chaos theory/Harmony search algorithm/Parameter selection分类
信息技术与安全科学引用本文复制引用
丁春莉,李林森..和声搜索算法优化支持向量机的网络流量预测[J].微型电脑应用,2017,33(1):67-70,4.
