基于连续小波神经网络的华北型煤田底板破坏深度预测研究

Study on Prediction of Floor Failure Depth of North China Coalfield Based on Continuous Wavelet Neural Network

根据全国典型煤矿底板破坏深度的实测资料,应用Matlab软件的分析功能,对煤层采深、倾角、采厚、工作面斜长、底板抗破坏能力五个在底板破坏中发挥主要作用的影响因子进行全面分析[1],利用小波神经网络对华北型煤田底板破坏深度进行了预测,发现基于连续小波神经网络的预测模型与实测结果更接近,能够在底板破坏深度预测中发挥借鉴作用。

Based on the measured data of the floor failure depth of typical coal mines in China, and using the analysis function of Matlab software, this paper makes a comprehensive analysis of five influencing factors which play a major role in floor failure, namely, mining depth, dip angle, mining thickness, face length and floor resistance to destruction. Wavelet neural network is used to predict the depth of floor failure of North China type coalfield, it is found that the prediction model based on continuous wavelet neural network is closer to the measured results. It can be used for a reference in predicting the depth of floor failure.