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A New Approach of Disaster Forecasting Based on Least Square Optimized Neural Network
Geofluids ( IF 1.2 ) Pub Date : 2020-11-07 , DOI: 10.1155/2020/8882241
Fanbao Meng 1 , Suolin Jing 1 , Xizhen Sun 2 , Changxiang Wang 1 , Yanbo Liang 1 , Da Pang 3
Affiliation  

The evaluation of the risk is the prerequisite for the implementation of countermeasures in the prevention and control of rock burst, and the research on the fast forecast at scene of the rock burst is more important for the safety production of coal mine. Aiming at the problem that dynamic disasters caused by many factors and heterogeneity of coal and rock are difficult to predict in the process of coal mining, in this paper, the general law and the risk control factors of the rock burst are studied, a mathematical model based on the BP neural network was built according to the different actual mining conditions in the mining area, and the output layer has obtained the prediction result. Then, the results of the output samples after training were fitted by using SPSS software, and the fitting function was obtained by multiple least square fitting. Finally, the fitting results are checked by the data of actual coal mine dynamic disaster parameters. The prediction results show that the simulation results of BP neural network prediction model and the fitting function of the least square method can reduce the impact of subjective judgment on the prediction results, and the application of the fitting function can obtain the prediction results in the first time to ensure the construction safety. The method of on-site hazard assessment and inspection by using fitting function is simple and feasible and has high accuracy, which provides a new idea for the field prediction of rock burst.

中文翻译:

一种基于最小二乘优化神经网络的灾害预测新方法

风险评估是冲击地压防治对策实施的前提,而冲击地压现场快速预报研究对煤矿安全生产更为重要。【摘要】:针对煤矿开采过程中因多因素和煤岩非均质性造成的动力灾害难以预测的问题,本文研究了岩爆的一般规律和风险控制因素,建立了数学模型。根据矿区不同的实际开采条件,构建了基于BP神经网络的输出层,得到了预测结果。然后使用SPSS软件对训练后输出样本的结果进行拟合,通过多重最小二乘拟合得到拟合函数。最后,通过实际煤矿动态灾害参数数据对拟合结果进行检验。预测结果表明,BP神经网络预测模型的仿真结果和最小二乘法的拟合函数可以降低主观判断对预测结果的影响,拟合函数的应用可以在第一时间得到预测结果。时间,以确保施工安全。利用拟合函数进行现场危险性评估和检查的方法简单可行、准确度高,为岩爆现场预测提供了新思路。预测结果表明,BP神经网络预测模型的仿真结果和最小二乘法的拟合函数可以降低主观判断对预测结果的影响,拟合函数的应用可以在第一时间得到预测结果。时间,以确保施工安全。利用拟合函数进行现场危险性评估和检查的方法简单可行、准确度高,为岩爆现场预测提供了新思路。预测结果表明,BP神经网络预测模型的仿真结果和最小二乘法的拟合函数可以降低主观判断对预测结果的影响,拟合函数的应用可以在第一时间得到预测结果。时间,以确保施工安全。利用拟合函数进行现场危险性评估和检查的方法简单可行、准确度高,为岩爆现场预测提供了新思路。
更新日期:2020-11-07
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