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Coalmine Gas Concentration Forecasting Based on Chaotic Theory and Neural Network Model

机译:基于混沌理论和神经网络模型的煤矿气体浓度预测

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Coalmine gas explosion is unique to the extremely serious type of disaster. The root cause of gas explosion accident is the Overrun of the gas concentration. Gas concentration is forecast to achieve effective prevention of gas explosion accidents. According to the non-linear of gas concentration and the predictability of the chaotic time series, gas concentration phase space was reconstructed by the Takens theory. In the first, the time delay was attained by the mutual information method. Secondly the embedding dimension was determined by GP algorithm and the chaotic time series was predicted by the BP neural network. Finally, an example is given which shows the forecast results could approximate the actual situation well, and accomplishing the forecast objection of gas concentration.
机译:煤矿气体爆炸对极其严重的灾难来说是独一无二的。气体爆炸事故的根本原因是气体浓度的超支。预测气体浓度是为了实现瓦斯爆炸事故的有效预防。根据气体浓度的非线性和混沌时间序列的可预测性,通过Takens理论重建气体浓度相空间。首先,通过相互信息方法实现了时间延迟。其次,通过GP算法确定嵌入尺寸,并通过BP神经网络预测混沌时间序列。最后,给出了一个例子,其示出了预测结果可以近似实际情况,并完成气体浓度的预测反对意见。

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