关键词: 网络流量, 最小二乘支持向量机, 模糊均值聚类, 训练集

Abstract: In order to improve the predicting accuracy of network traffic, this paper proposes a network traffic predicting model（FCM-LSSVM） based on Least Square Support Vector Machine（LSSVM） and selecting train sample. Firstly, the Fuzzy C-Means（FCM） clustering algorithm is used to analyze the network traffic data to select the training samples and eliminate the isolated samples, and then the train samples are input to LSSVM to learn and the artificial bee colony algorithm is used to optimize the parameters of model, and finally, the predicting model is established and the simulation experiment is carried out to test the performance of the model. The simulation results show that the proposed has improved predicting accuracy of network traffic and speeding up modeling speed compared with other network traffic predicting models, so it can obtain the good predicting results.

Key words: network traffic, Least Squares Support Vector Machine（LSSVM）, Fuzzy C-Means（FCM） clustering, training samples