摘要
鉴于人工蜂群算法(ABC)算法和人工鱼群(AFS)算法本身的优势,为提高预测精度,提出基于ABC+AFS-LSSVM的网络流量预测模型.运用基于ABC+AFS-LSSVM的模型对网络流量进行了预测,并与ABC-LSSVM、AFS-LSSVM和PSO-LSSVM模型的预测结果进行了比较,结果表明,基于ABC+AFSLSSVM的网络流量预测模型预测精度较高,具有更好的性能及应用前景.
In view of the advantages of the artificial swarm(ABC) algorithm and the artificial Shoal(AFS) algorithm itself, a network flow prediction model based on ABC+AFS-LSSVM is proposed to improve the prediction accuracy. The model based on ABC+AFS-LSSVM was used to predict the network traffic and compared with the predicted results of ABC-LSSVM, AFS-LSSVM and PSO-LSSVM models. The results show that the network flow prediction model based on ABC+AFS-LSSVM has high prediction precision, better performance and better application prospects.
作者
孙群
袁宏俊
SUN Qun;YUAN Hong-jun(Department of Basic,Anhui Vocational College of Electronics Information Technology,Bengbu Anhui 233030,China;College of Mathematics and Computer Science,Anhui University,Hefei Anhui 230039,China;College of Statistics and Applied Mathematics,Anhui University of Finance Economics,Bengbu Anhui 230039,China)
出处
《淮阴师范学院学报（自然科学版）》
CAS
2019年第2期124-129,共6页
Journal of Huaiyin Teachers College;Natural Science Edition
基金
安徽省教育厅高校社会科学基金项目(SK2018A0431)
关键词
人工蜂群算法
人工鱼群算法
最小二乘支持向量机
网络流量
预测
artificial bee colony
artificial fish swarm
least square support vector machine
network flow
prediction
