The Research Of Comprehensive Evaluation Model Of Coal Mine Safety Based On Neural Network

Firstly, this paper analyzes the status quo of Chinese coal mine safety, and it is necessary to carry out a comprehensive evaluation for coal mine. Based on safety accident causation theory and the complexity of coal mine system, three methods which commonly used in safety analysis, fault tree analysis, Human-Machine-Environment analysis and analytic hierarchy process, are synthesized to build the indicators analysis model of coal mine safety, by which we could get the factors that affect safety status of coal mine.Through the analysis model, indicators system is built, which is vertically divided into the target layer, middle layer and indicators layer. And in the indicators layer, there are 34 indicators horizontally ranging in four areas, the personnel quality part, equipment part, environmental factors and safety management part.Then make an introduction of the neural networks' structure, algorithms and so on, for the existence of local minimum of the BP neural network learning algorithm, this paper adopt an additional momentum method to improve it's efficiency. Then use the improved BP neural network model to build comprehensive evaluation model of coal mine safety with three layers of 34-14-5 structure, and program the model in the computer by the neural network toolbox of MATLAB. After that, train the neural network with 20 representative samples, we find out that it is slowly the training error function curve converges, then the method of self-adaptively adjusting steps is obtained in the paper.Finally, five instances of Yima Coal Industry Group are used for simulation, and the result shows that the model has successfully established the complex nonlinear mapping relationship between safety indicators and the actual safety situation of the mines.With the abundance of the sample library in future, this model will get more accurate evaluation results, and its application value will be enhanced.