基于相关向量机的网络通信负载状态识别模型

Identification Model of Network Communication Load State Based on Relevance Vector Machine

为了改善网络通信负载状态识别效果,提出一种基于相关向量机的网络通信负载状态识别模型.首先提取影响网络通信质量的参数,分析它们与负载状态间的联系;然后将无线传感器网络吞吐率作为负载状态识别的标准,采用相关向量机构建网络通信负载状态的分类器,实现网络通信负载状态的识别;最后采用具体数据对网络通信负载状态识别性能进行测试.测试结果表明,相关向量机可准确识别网络通信负载状态,且网络通信负载状态识别正确率高于其他模型.

In order to improve the identification effect of network communication load state,we proposed a identification model of network communication load state based on relevance vector machine.Firstly,the parameters which affected the quality of network communication were extracted,and the relationship between them and their load states was analyzed.Secondly,the throughput of wireless sensor network was regarded as the standard of load state identification,and the relevance vector machine was used to construct the classifier of network communication load state to realize the identification of network communication load state.Finally,the specific data was used to test the performance of network communication load state identification.The test results show that the relevance vector machine can accurately identify the network communication load state,and the correct rate of the network communication load state identification is higher than other models.