【摘要】 针对VBR视频流量的时变性、突发性和非线性等特点,提出一种基于小波支持向量机的VBR视频流量预测模型(WSVM)。首先对VBR视频流量时间序列进行相空间重构,然后将其输入到小波支持向量机进行学习,建立VBR视频流量预测模型,最后采用仿真实验对模型性能进行测试。结果表明,相对于对比模型,WSVM提高了VBR视频流量预测精度,更加准确地描述了VBR视频流量的复杂变化特点。更多还原

【Abstract】 Because VBR video traffic has time-varying and nonlinear,a prediction model of VBR video traffic based on wavelet support vector machine( WSVM) is presented. Firstly,VBR video traffic time series are reconstructed by the phase space reconstruction,and then the reconstructed VBR video traffic time series are input into WSVM to learn and establish the prediction model of VBR video traffic,finally the simulation experiment is carried out to test the performance of the model. The simulation results show that,compared with other models,WSVM has improved the prediction accuracy of VBR video traffic,and it can more accurately describe the complex change rules of VBR video traffic.更多还原