文章摘要
曲桦,马文涛,赵季红,王涛.基于最大相关熵准则的网络流量预测[J].高技术通讯(中文),2013,23(1):
基于最大相关熵准则的网络流量预测
Network traffic prediction based on maximum correntropy criterion
  修订日期:2012-02-23
DOI:
中文关键词: 最大相关熵准则(MCC), 最小均方误差(MMSE), Elman神经网络, 网络流量预测
英文关键词: maximum correntropy criterion (MCC), minimum mean square error (MMSE), Elman neural network, traffic network prediction
基金项目:国家自然科学基金(61071126)和国家无线重大专项(2010ZX03004-001, 2010ZX03004-002, 2011ZX03002-001)资助项目
作者单位
曲桦 西安交通大学电信学院 
马文涛 西安交通大学电信学院 
赵季红 西安交通大学电信学院 
王涛 西安交通大学电信学院,西安邮电大学通信与信息工程学院 
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中文摘要:
      为提高网络流量预测的精度,针对网络流量的非线性特征提出了一种基于新的误差评价准则——最大相关熵准则(MCC)的网络流量预测方法。该方法使用MCC对Elman神经网络进行训练。该评价准则是基于新的相似度函数——广义相关熵(correntropy)函数的概念建立的,此相似度函数以误差概率密度函数的Parzen窗估计和瑞利熵为基础。同时结合MCC和最小均方误差(MMSE)准则提出了一种混合的评价准则MCC-MMSE。针对网络流量的非线性、非高斯性、突变性等特性,分别以MCC、MCC-MMSE准则进行了Elman神经网络的训练,使用训练好的神经网络进行网络流量预测,仿真结果表明预测结果的精度优于以MMSE为准则的Elman神经网络的预测结果。
英文摘要:
      With the nonlinear characteristics of network traffic considered, a new network traffic prediction method based on the maximum correntropy criterion (MCC), a new error evaluation criterion, was proposed to improve the precision of traffic network prediction. The method uses the MCC to train Elman neural networks, and this evaluation criterion is based on the new concept of a new similarity function, the generalized correlation entropy (correntropy) function, which takes the Parzen window estimation of the error probability density function and the Rayleigh entropy as the basis. Simultaneously, a mixed evaluation criterion which combines the MCC and the minimum mean square error (MMSE) criterion was presented. In view of the characteristics of traffic networks such as the nonlinear, non Gauss, and mutation, the Elman neural network was trained by the MCC and the mixed criterion, respectively, and then a trained neural network was used to predict network traffic. The simulation results show that the accuracy of the prediction is superior to the prediction results of the Elman neural network with the MMSE criterion.
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