基于聚类分析算法和优化支持向量机的无线网络流量预测

Prediction of Wireless Network Traffic Based on Clustering Analysis and Optimized Support Vector Machine

为了解决当前无线网络流量预测过程存在的一些问题,以提高无线网络流量的预测精度为目标,提出基于聚类分析算法和优化支持向量机的无线网络流量预测模型。首先,采集无线网络流量数据集,并采用聚类分析算法构建训练样本集合;然后,采用支持向量机对无线网络流量训练样本进行学习,并引入布谷鸟搜索算法对支持向量参数进行优化,从而建立无线网络流量预测模型;最后,通过具体无线网络流量预测实例分析模型的有效性。结果表明,所提模型的无线网络流量预测精度高,提升了无线网络流量建模效率,而且其无线网络流量预测效果要优于当前经典无线网络流量预测模型,具有比较显著的优越性。

In order to solve the problems existing in the current wireless network traffic prediction process and improve the accuracy of wireless network traffic prediction,a wireless network traffic prediction model based on clustering analysis algorithm and optimized support vector machine is proposed.Firstly,data sets of wireless network traffic are collected and clustering analysis algorithm is used to construct the training sample set.And then,support vector machine is used to learn the training samples of wireless network traffic,and cuckoo search algorithms is introduced to optimize the parameters of support vector.Thus,the prediction model of wireless network traffic is established.Finally,the effectiveness of the model is analyzed through a specific example of wireless network traffic prediction.The results show that the proposed model has high prediction accuracy,improves the efficiency of wireless network traffic modeling,and the prediction effect of wireless network traffic is better than the current classical wireless network traffic prediction models,which has more significant advantages.