基于相关向量机的网络通信负载状态识别模型

发布时间：2018-04-24 20:34

  本文选题：无线传感器网络 + 网络通信负载　； 参考：《吉林大学学报(理学版)》2017年06期

【摘要】：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.
[Abstract]:In order to improve the recognition effect of network communication load state, a network communication load state recognition model based on correlation vector machine is proposed. Firstly, the parameters affecting the network communication quality are extracted, and the relationship between them and the load state is analyzed. Then, the throughput of the wireless sensor network is regarded as the standard of load state identification, and the classifier of the network communication load state is constructed by using the correlation vector mechanism. Finally, the identification performance of network communication load state is tested with specific data. The test results show that the correlation vector machine can accurately identify the network communication load state, and the correct recognition rate of network communication load state is higher than that of other models.
【作者单位】： 吉林农业大学信息技术学院;
【基金】：吉林省教育厅“十二五”科研项目(批准号:201452)
【分类号】：TN929.5;TP181;TP212.9
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本文编号：1798155