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BP neural network model on the forecast for blasting vibrating parameters in the course of hole-by-hole detonation 被引量:4

BP neural network model on the forecast for blasting vibrating parameters in the course of hole-by-hole detonation
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摘要 According to the neural network theory, combined with the technical characteristicsof the hole-by-hole detonation technology, a BP network model on the forecast forblasting vibration parameters was built.Taking the deep hole stair demolition in a mine asan experimental object and using the raw information and the blasting vibration monitoringdata collected in the process of the hole-by-hole detonation, carried out some training andapplication work on the established BP network model through the Matlab software, andachieved good effect.Also computed the vibration parameter with the empirical formulaand the BP network model separately.After comparing with the actual value, it is discoveredthat the forecasting result by the BP network model is close to the actual value. According to the neural network theory, combined with the technical character- istics of the hole-by-hole detonation technology, a BP network model on the forecast for blasting vibration parameters was built. Taking the deep hole stair demolition in a mine as an experimental object and using the raw information and the blasting vibration monitoring data collected in the process of the hole-by-hole detonation, carried out some training and application work on the established BP network model through the Matlab software, and achieved good effect. Also computed the vibration parameter with the empirical formula and the BP network model separately. After comparing with the actual value, it is discov- ered that the forecasting result by the BP network model is close to the actual value.
出处 《Journal of Coal Science & Engineering(China)》 2010年第3期249-255,共7页 煤炭学报(英文版)
基金 Supported by the National Natural Science Foundation of China(50778107)
关键词 逐孔起爆技术 网络预测模型 爆破振动 神经网络模型 过程参数 BP网络模型 MATLAB软件 基点 blasting vibration, BP neural network, detonation hole-by-hole, prediction model
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