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阐述了矿井小断层走向延展长度预测Elman神经网络模型的构建、训练及模拟方法,结合实测样本数据,利用灰色关联分析法分析了小断层走向延展长度与断层落差、走向、倾角及倾向等影响因素的相关性,确定了小断层走向延展长度的预测参数,并运用matlab软件建立了基于Elman神经网络的矿井小断层走向延展长度预测模型。实际应用表明,该模型的预测精度较高,比较符合实际情况。
The construction, training and simulation methods of Elman neural network model for predicting the extension direction of small faults are described. Based on the measured data and the gray relational analysis method, the influencing factors such as the strike length of faults, the strike, dip and inclination of faults are analyzed The prediction parameters of the extension of the small fault are determined, and the mathematic model of predicting the extension direction of the small fault in the mine based on Elman neural network is established. Practical application shows that the prediction accuracy of the model is higher and more in line with the actual situation.