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.
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