Coal Mine Safety Dynamic Evaluation And Combination Forecast Model

The coal and mine system's security status could be predicted accurately and efficiently by researching into nature of coal mine safety forecast. Based on actuality and means of present research on coal mine safety, research on internal regularity and effective time of coal mine safety forecast has been done in order to summarize substantive characteristics on coal mine safety forecast and study modeling process of safety forecast.According to experience gained from previous coal mine safety evaluation, combined with present status of development of coal mine safety evaluation and forecasting at home and abroad, coal mine safety evaluation and forecasted model is built by utilizing data collected on scene of coal mine.Based on neural network and fuzzy neural network, neural network model of coal mine safety evaluation is built. That model is better at parallel computing, complex data processing and ability of learning from itself.A suit of integrated targeted system used for evaluation which can reflect nature of evaluated object objectively will be mainly researched. That targeted system is wished to include all the factors which can affect coal mine safety production. In fact, not all the factors could be covered. This targeted system can be employed in model of neural network and fuzzy neural network of coal mine safety and be studied in applied aspect. The mining well security is dynamically forecasted by mainly utilizing the ability that neural network can forecast timing targets.Finally, coal mine safety forecasting system will be built combined with concrete examples. Nonlinear combination forecast model has very high accuracy and nonlinear combination is better than linear one. Nonlinear combination forecast model can overcome the shortcoming of single forecast model. Then difficulty in forecasting caused by stochastic alternation of targeted value for system safety can be solved. Forecast model with fuzzy neural network and BP feedback neural network will be used.Figure 34 table 11 references 50...