多分簇网络流量的小波尺度粒子群变步长检测

Wavelet Scale Particle Swarm Variable Step Detection of Multi Clustering Network Traffic

多分簇网络是蜂窝通信和移动数据传输的混合产物,多分簇网络流量具有时变耦合特性,传统方法采用功率谱局部特征分析方法进行流量的特征检测,效果不好。提出一种基于小波尺度耦合和粒子群优化分析的多分簇网络变步长检测算法,采用粒子群优化算法进行多分簇网络流量的特征提取和编码分析,采用小波尺度耦合方法变步长检测,引入小波变换,进行流量序列的尺度耦合分析,采用自适应变步长方法去除流量特征的虚假分量。仿真结果表明,采用算法进行网络流量的检测,能有效识别不同尺度下的网络流量特征,在流量预测中,通过变步长自适应控制,使得收敛速度很快,流量准确预测精概率为1,检测性能较好。

Multi cluster network is a mixed product of cellular communications and mobile data transmission, multi cluster network traffic with time-varying coupling characteristics, traditional methods of the power spectrum local feature analysis method for the detection of flow characteristics, the effect is not good. A step detection algorithm based on wavelet multi-scale coupling and particle group optimization analysis of multi cluster network, the particle swarm optimization algorithm for multi cluster network traffic feature extraction and coding analysis, variable step size detection using wavelet multi-scale coupling method, the introduction of wavelet transform, scale coupling analysis of flow series is conducted, using adap-tive variable step size method to remove false component flow characteristics is proposed in this paper. Simulation results show that the algorithm for the detection of network traffic, it can effectively identify different scale network traffic charac-teristics, in the traffic prediction, the variable step size adaptive control, the convergence speed is very fast and accurate flow forecasting precision with probability 1, it has good detection performance.