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煤与瓦斯突出是影响煤矿安全生产的重大灾害之一,由于其动力现象的复杂性,因此具有非线性系统的特征。针对某掘进工作面的2组瓦斯涌出数据,应用非线性理论对其进行研究,以期得到预测突出的新方法。首先分析了时间序列的来源,对经过预处理的涌出瓦斯时间序列进行相空间重构,分析和证明了该序列具有混沌与分形特征,并在此基础上,建立了瓦斯突出的混沌和分形特征的的神经网络预测模型。
Coal and gas outburst are one of the major disasters that affect the safety of coal mines. Due to the complexity of their dynamic phenomena, they have the characteristics of a nonlinear system. Aiming at two groups of gas emission data of a heading face, the nonlinear theory is used to study the data, in order to get a new forecasting method. Firstly, the source of the time series is analyzed, the phase space of the pre-treated gushed gas time series is reconstructed, and the chaotic and fractal characteristics of the sequence are analyzed and proved. Based on this, the gas chaos and fractal Characteristics of the neural network prediction model.