RBF神经网络在爆破振动强度预测中的应用APPLICATION OF RBF NEURAL NETWORK IN THE BLASTING VIBRATION STRENGTH PREDICTION
任岩;
REN Yan (College of Mining Technology,Taiyuan University of Technology,Taiyuan 030024,China)
摘要(Abstract)：
在介绍RBF神经网络基本思想的基础上,建立了爆破振动预测模型,用RBF神经网络方法对质点振幅、主振频率及振动持续时间进行预测。用阳泉煤矿主井爆破开挖工程中所监测到的振动数据对模型进行了训练,并对27组数据进行了预测,实测结果和模型预测结果的对比表明,RBF神经网络预测模型能反映影响因素与特征量之间的非线性关系,适用于爆破振动特征参量预测。
Based on introducing the basic idea of RBF neural network,the prediction model of blasting vibration was built.The vibration amplitude,main vibration frequency,and vibration duration time of the particles were predicted with the RBF neural network.Blasting vibration data measured in main shaft excavation of the Yang Quan coal mine was used to train the model and 27 sets of data were predicted by using the RBF trained.The comparison of measured results and predicted results showed that RBF neural network prediction model could reflect the non-linear relationship between influencing factors and characteristic parameters,and it could be suitable for characteristic parameters prediction of blasting vibration.
关键词(KeyWords)：
RBF神经网络;爆破振动;强度预测;误差分析
RBF neural network;Blasting vibration;Strength prediction;Error analysis
基金项目(Foundation):
作者(Authors):
任岩;
REN Yan (College of Mining Technology,Taiyuan University of Technology,Taiyuan 030024,China)
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