Study On Models And Application Of Dynamical Overall Safety Evaluation In Coalmines

Based on the distinguished young scholar project mining environment and safety, which wasfunded by the National Natural Science committee, this paper has been studied for the sake ofscientific, convenient and adaptable mine safety evaluation methods.Firstly, in the paper, in order to put forward the importance and necessity of safety evaluation,Chinese coalmine safety status is analyzed. The domestic and foreign safety evaluations aresummarized to analyze the problem of traditional coalmine safety evaluation methods, andbring forward Artificial Neural Networks (ANN) to deal with many problems, such asnonlinear, changeable weights, etc.Secondly, based on the Accidental Incidence Theory and other safety theories, the AnalyticHierarchy Process are combined with other methods to analyze the primary factors affectingcoal mine safety; furthermore, the processes are classified into 11 types including human,mechanical, environmental factors. And the evaluation index pretreatment methods are givento establish the coal mine safety evaluation index system.Thirdly, because ANN are adaptive models that can be learned from the data and generalizethings. Especially in contrast to traditional models, which are theory-rich and data-poor, theneural networks are data-rich and theory-poor in a way that a little or no prior knowledge ofthe problem is present. Neural networks (NN) can be used for coalmine safety evaluationfrom inputs to outputs of these kinds of black boxes. In this paper, the multilayer model isestablished, which learn using an algorithm called back propagation.Fourthly, the coal mine safety evaluation BP model is designed by the use of the NN tool boxof the MATLAB software, trained by the means of safety sample data of 33 mines to provethat the ANN model is applicable for coal mine safety evaluation. Moreover, the process and characteristics of coal mine safety evaluation BP model are analyzed to put forth the new method(FNN) integrating the fuzzy mathematics and ANN, which has the same nonlinear and changeable weights processing function as ANN, and the same functions of using expertise and little requiring sample data as the fuzzy mathematics. The coal mine safety evaluation FNN model has been set up and applied to 10 mine sample data.At last, safety forecasting model are also founded by the MATLAB software. By comparing three types of NN models and training functions, it is proved that the TRAINDM learning rules continue for too long; but the TRAINBR can minimize the error than TRAINLM and not continue such length as TRAINDM. Therefore, TRAINBR maybe is the most adaptive learning rule for coalmine safety forecasting. Besides, if the forecasting model is so simple, it is hard to meet with the precise requirements.The paper has two creative points: one is that the FNN are firstly used to evaluate coal mine overall safety conditions; the other is that three types of NN structures and training functions are comparably studied to prove that the TRAINBR function maybe is the more adaptive learning rule of coalmine safety forecasting than the TRAINLM function, and if the forecasting model is so simple that it can not meet with the precise requirements.