Prediction of mine coal layer spontaneous combustion danger based on genetic algorithm and BP neural networks

摘要

The prediction of danger state in mine coal layer spontaneous combustion correctly has an important practical meaning for the mine production safety. There is a kind of complicated nonlinear relation between the danger of coal layer spontaneous combustion and its influencing factors. And the neural network can truly show the nonlinear relation. In this paper, for the purpose of predicting it in mine coal layer correctly, a kind of method combined the advantages of genetic algorithm (GA) and BP neural network is introduced based on the demonstration of the necessity and possibility of combining BP with GA. At first, the notion of using multilayered BP as the representation method of genetic and the searching technique is introduced, and a novel method of using GA to train connection weights of BP neural network is designed. According to the characteristics of mine coal layer spontaneous combustion danger, three key influencing factors are selected as the judging indexes. Then the model for predicting the danger of mine coal layer spontaneous combustion is built. Practical application indicates that the capability of the new method in fast learning of ANN and escaping local optima. And the results show that the model is a very efficient method for predicting the danger of coal layer spontaneous combustion.