基于混沌的煤矿监测网络流量异变的预测

Prediction of Mine Monitoring Network Ttraffic Mutation Based on Chaos Theory

  • 摘要: 对煤矿监测网络流量异变预测可以提高网络的服务质量(QoS),降低网络拥塞的发生率。该文分析了煤矿监测网络中各信息流量的特点,提出了以SCADA类信息流作为混沌指标信号,采用Lyapunov指数法验证指标量的混沌特性,利用Duffing振子求解相变点的策动力幅值ad并构建了预测模型。通过仿真预测和实测数据比较,误差在±0.036 632之间,验证了该预测方法准确可靠。

     

    Abstract: Prediction of the traffic mutation in network for mine monitoring can enhance quality of service (QoS) and reduce network congestion. In view of the characteristics of traffic in mine monitoring network, data acquisition (SCADA) information is selected as indicator of chaotic signal and Lyapunov exponent method is used to verify the chaotic characters of the indicator. Duffing oscillator is employed to calculate the amplitude of the critical threshold of the oscillator ad and a predicting model is constructed. The error between simulation results and real data is between ±0.036 632, validating the accuracy and reliability of the predicting method.

     

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