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The research of fuzzy dynamic Bayesian network in cognitive network QoS student study assessment

机译:模糊动态贝叶斯网络在认知网络QoS学生学习评估中的研究

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In terms of network operating QoS analysis, we propose a cascading network QoS analyzing algorithm based on K-means and C4.5 algorithm. The algorithm was testified to be suitable for multiple analyze requirements. Also based on the monitoring data captured from the real Internet, the algorithm was proved to be effective and efficient The algorithm processes the data captured from the project “User-oriented Active 4G Network Measurement System. The system is deployed at multiple wireless network accessing points; conducting 4G network QoS monitoring 24 hours. The paper covers experiments to find a proper K and C4.5 discrete value, and use the KKZ algorithm to initialize the cluster center values. Based on the six traces of monitoring data, we compared the performances of cascading network QoS analyzing algorithm, K-mean algorithm and C4.5 algorithm. As a result, the cascading algorithm was highly efficient and reduces the noise of single algorithm; also it proved to be suitable to several types of monitoring data.
机译:在网络运行QoS分析方面,提出了一种基于K均值和C4.5算法的级联网络QoS分析算法。经过验证,该算法适用于多种分析需求。另外,基于从真实互联网捕获的监视数据,该算法被证明是有效且高效的。该算法处理从“面向用户的有源4G网络测量系统”项目中捕获的数据。该系统部署在多个无线网络接入点。 24小时进行4G网络QoS监控。本文涵盖了寻找合适的K和C4.5离散值,并使用KKZ算法初始化聚类中心值的实验。基于监控数据的六条轨迹,比较了级联网络QoS分析算法,K-mean算法和C4.5算法的性能。结果,该级联算法是高效的并且减少了单个算法的噪声。它也被证明适用于多种类型的监视数据。

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