摘要
随着开采深度的增加,影响巷道围岩稳定性的因素更多,更复杂,并且更具有随机性。为了对深井巷道围岩稳定性进行有效的分类,考虑影响围岩稳定性因素的随机性,建立了围岩稳定性判定的概率神经网络模型(PNN),并利用MATLAB实现其识别过程。根据样本数据对所建模型进行训练后,将其运用于实际工程中的深井巷道围岩进行稳定性判定。研究表明:利用概率神经网络模型可对地下工程深井巷道围岩稳定性进行分析,概率神经网络模型为地下工程围岩稳定性分类提供了一种方法。
With the increase of mining depth,Factors that effect the stability of surrounding rock more and more complex.In order to carry out effective classification for stability of surrounding rock,Thinking about the effective factors＇ Probabilistic,Establishing the stability of surrounding rock to determine the PNN neural network model,And utilizing MATLAB to achieve its recognition process.According to sample data on the model for training,will be the actual projects to determine the stability of surrounding rock.Research shows that：Selected indicators in line with the stability of surrounding rock engineering practice;Built by PNN neural network model of the stability of surrounding rock Sham accurate analysis.
出处
《武汉工业学院学报》
CAS
2010年第2期67-70,共4页
Journal of Wuhan Polytechnic University
基金
国家自然科学基金项目(50574072
50534049)
陕西省教育厅专项科研基金项目(08JK366)
