粗糙集-BP神经网络组合方法及其应用

Method of rough sets-back propagation neural network and its application to identification of surrounding rock stability

将神经网络与数据挖掘的知识相结合,提出粗糙集-BP神经网络组合方法,并将其应用于围岩稳定性判别。首先,基于山东兖州矿区煤巷信息数据库,建立回采巷道围岩稳定性知识表达系统,对数据进行离散化处理;其次,针对传统BP神经网络收敛速度慢、容错性差、结果不唯一的缺点,采用MATLAB软件开发的粗糙集数据分析程序,对生成的决策表进行挖掘分析,通过挖掘的决策知识引导训练样本的选取和神经网络的建立;最后,在煤巷围岩稳定性判别中予以应用。研究结果表明:BP神经网络克服了传统BP神经网络的缺点,具有容错性好、训练速度快、全局逼近性好、精度高等优点,此方法能较好地用于解决巷道围岩稳定性判别问题。

Combining neural network with data mining,the identifying method of surrounding rock stability was brought forward.Firstly,a knowledge expression system based on information database of Yanzhou Coal Minerial roadway was founded,and its data were discretized.Secondly,aiming at the defects of traditional BP neural network,namely slow convergence,poor fault tolerance and inconsistent result,decision table was obtained by rough sets data analysis program compiled by MATLAB software;afterwards,decision knowledge was used to conduct constitution of neural network and training samples selection.In the end,the stability of surrounding rock was studied.The results show that this method conquers many disadvantages of traditional BP neural network,and possesses many merits,such as well fault tolerance,fast education speed,high precision,good universal approximation,and so on.Therefore,it can be preferably used to identify the stability of surrounding rock.