和声搜索算法优化支持向量机的网络流量预测

摘要

网络流量受到外界因素作用,具有复杂的变化规律,为了改善了网络流量的预测效果,设计了和声搜索算法优化支持向量机的网络流量预测模型(HS-SVM).首先对当前网络流量预测研究现状进行深入分析,并指出了网络流量的混沌特性,然后采用混沌理论的相应方法确定网络流量的延迟时间和嵌入维数,并对原始网络流量数据进行重构,最后采用HS-SVM建立网络流量预测模型,并与当前其它网络流量预测模型进行了对照模拟测试.HS-SVM能够挖掘和分析网络流量的变化规律,网络流量预测结果要明显优于其它网络流量预测模型,测试结果验证了HS-SVM的可行性和优越性.%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.