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为了有效刻画实际业务流性能状态,结合分形布朗运动模型(Fractional Brownian Motion,FBM)和元胞自动机提出一种新的预测方法 TSPCA(Traffic State Prediction method based on Cellular Automaton).该方法首先基于FBM模型推导了平均队列长度和平均时延的数学表达式,同时利用定义的元胞演化规则对估算结果进行修正,以提高预测精度.最后,通过NS2和MATLAB进行仿真实验,深入分析了影响该方法的关键因素,发现缓冲区较小时流量性能将由短相关特性支配,而缓冲区较大时性能由长相关支配,重置效应和截断效应对业务流性能影响较大.并且对比FARIMA和ARIMA的预测结果,证明该方法具有较好的适应性.
In order to effectively describe the actual traffic performance state, a new prediction method TSPCA (Traffic State Prediction method based on Cellular Automaton) is proposed based on Fractional Brownian Motion (FBM) and cellular automata The model deduces the mathematical expression of average queue length and average delay, and modifies the estimation results by using the defined cellular evolution rules to improve the prediction accuracy.Finally, through NS2 and MATLAB simulation experiments, in-depth analysis of the impact of the method , It is found that the traffic performance will be dominated by short correlation when the buffer is small and the performance is controlled by the long correlation when the buffer is large and the reset effect and truncation effect greatly affect the performance of the traffic flow.Compared with the prediction of FARIMA and ARIMA The result proves that this method has better adaptability.