Application of Grey Relation Clustering and CGNN in Analyzing Stability Control of Surrounding Rocks in Deep Entry of Coal Mine

摘要：

With combined grey neural network (CGNN) and grey relation clustering, the models are setup, which is to solve the prediction and comparison of surrounding rocks stability controlling parameters in deep entry of coal mine. The results show that grey relation clustering is an effective way and CGNN has perfect ability to be studied in short-term prediction. Combined grey neural network has the features of trend and fluctuation while combining with the time-dependent sequence prediction. It is concluded that great improvement comparing with any methods of trend prediction and simple factor in combined grey neural network is stated and described in stably controlling the surrounding rocks in deep entry.

作者: Wanbin YANG Zhiming QU

作者单位: Institute of Civil and Environmental Engineering, Beijing University of Science and Technology, Beij School of Civil Engineering, Hebei University of Engineering, Handan, 056038, China

会议类型: 国际会议

会议名称: 2009 IEEE International Conference on Grey System and Intelligent Services(2009 IEEE灰色系统与服务科学国际会议)

会议地点: 南京

会议语种:英文

页码: 186-190

在线出版日期: 2009-10-20（万方平台首次上网日期，不代表论文的发表时间）