布谷鸟优化混合核相关向量机的网络流量预测

Network Traffic Prediction Model Based on Mixed Kernels RVM Optimized by CS Algorithm

为了提高网络流量的预测精度,提出一种布谷鸟算法优化混合核相关向量机的网络流量预测模型(CS-RVM)。首先采用多项式和高斯核函数构成混合核函数代替相关向量机的单一核函数,然后引入布谷鸟算法对混合核参数进行寻优,最后建立网络流量预测模型。仿真结果表明,CS-RVM具有良好的建模效果,可提高网络流量的预测精度。

In order to improve the prediction accuracy of network traffic , a network traffic prediction model is proposed based on cuckoo searching algorithm optimizing the parameters of mixed kernel relevance vector machine （ CS-RVM） to solve limitations of single kernel function for relevance vector machine .Firstly, the polynomial and Gaussian kernel functions are produced to mixed kernel function for the relevance vector machine , and then the cuckoo searching algorithm is introduced to optimize the parameters of hybrid kernel function , finally network traffic prediction model is established based on the relevance vector machine using the optimal parameters .The simulation results show that , CS-RVM model is of good effect and could improve the prediction accuracy of network traffic .