Application of Grey Relation Clustering and CGNN in Gas Concentration Prediction in Top Corner of Coal Mine

Using grey relation clustering and combined grey neural network (CGNN), the combined model is setup, which aims at solving the problems of predicting and comparing the gas concentration in top corner of coal mine. Through comparison and prediction, the results show that, in short-term prediction, grey relation clustering is an effective way and CGNN has perfect ability to study. CGNN has the dual properties of trend and fluctuation under the condition of combining with the time-dependent sequence prediction. It is concluded that great improvement comparing with any methods of trend prediction and simple factor in CGNN is stated and described in gas concentration in top corner of coal mine.