A NEW NETWORK TRAFFIC PREDICTION MODEL IN COGNITIVE NETWORKS

摘要：

With the development of the network technology and the increasing demands on communication, more complex, heterogeneous, and suitable network structures are right on their way to come. Cognitive networks can perceive the external environment; intelligently and automatically change its behavior to adapt the environment. This feature is more suitable to provide security for users with QoS. This paper proposes a hybrid traffic prediction model, which trains BPNN with Ant Colony Algorithm based on the analysis of the present models, in order to improve the cognitive feature in the cognitive networks. The proposed model can avoid the problem of slow convergence speed and an easy trap in local optimum when coming up with a fluctuated network flow. At the beginning, the model rejects the abnormal traffic flow data, and then use wavelet decomposition, in the following steps, the model predicts the network traffic with the hybrid model. Thus, the traffic prediction with high-precision in cognitive networks is achieved.

关键词： Cognitive networks Ant colony algorithm Neural network Wavelet BP (back propagation) neural network Network traffic prediction

作者: Dandan Li Runtong Zhang Xiaopu Shang

作者单位: The Institute of Information Systems, Beijing Jiaotong University, Beijing, China

会议类型: 国际会议

会议名称: 13th International Conference on Enterprise Information System(第13届企业信息系统国际会议 ICEIS 2011)

会议地点: 北京

会议语种:英文

页码: 2306-2314

在线出版日期: 2011-06-08（万方平台首次上网日期，不代表论文的发表时间）