Application of Grey Neural Network in Analyzing Disaster Prevention and Control in Coal Mine Based on CC and RBF-DDA Algorithms

Abstract

Prevention and control of the disastrous accident is the top priority of coal mine production safety. RBF and the combined grey neural network (CGNN) model are established. Combined with cascade-correlation (CC) and RBF-DDA algorithms, gas explosion impacting on coal mine production safety largely is analyzed. The analysis results show that gas explosion accident is caused by many reasons. The relationship between coal mine production and safety needs to be effectively coordinated. It is concluded that, at the beginning, CC and RBF-DDA algorithms are used to structure the initial hidden nodes to zero. Through the training process, the hidden units are added in the light of adaptive algorithm constantly. These units are of a higher classification accuracy and robustness, which, in the future, provides the basis for the deep application and study in coal mine safety and production.