摘要
本文应用BP神经网络对水泥矿山爆破块度进行仿真预测研究,使用Matlab软件建立一个3层前馈型BP神经网络模型,并在网络模型训练完毕后进行实测样本的仿真预测验证。试验结果表明,应用BP神经网络模型预测矿山爆破块度分布完全可行,且具有较高的精度;网络模型还可在矿山爆破参数优化设计方面起到辅助验证的作用。
The Back-Propagation Neural Network is utilized to research rock fragmentation prediction in cement mine. The paper establishes a three-layer feed forward BP Neural Network model by Matlab sotiware. After the model is trained it is used to simulate and predict measured sample .The prediction result proves that using the model to predicate rock fragmentation distribution is completely feasible and has better precision. The model also can be used to auxiliary verify optimal design of mine blasting parameters.
出处
《水泥技术》
2015年第1期36-39,共4页
Cement Technology
关键词
BP神经网络
爆破块度
仿真预测
爆破参数优化
back-propagation neural network
rock fragmentation
simulation and prediction
blasting parameters optimization
