Application of grey relational clustering and CGNN in analyzing stability control of surrounding rocks in deep entry of coal mine

With combination of grey neural network (CGNN) and grey relational clustering, the models are constructed, which are used to solve the prediction and comparison of surrounding rocks stability controlling parameters in deep entry of coal mine. The results show that grey relational clustering is an effective way and CGNN has perfect ability to be studied in a 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 improvements compared 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.