Mine Ventilation Safety Assessment Based on FNN and D-S Evidence Theory

This paper introduces an information fusion methodology, which is based on fuzzy neural network (FNN) and D-S evidence theory, to assess the mine ventilation system safety. This method imports fuzzy rule information, expert language information, etc. to fusion system by using fuzzy neural network, and uses the output of each neural network as the base probability assignment function (BPAF) of D-S evidence theory, and fuses this with the BPAF according to the combination rule of D-S evidence theory, which gives the assessment of the ventilation system. This method improves the systemic anti-jamming ability, and tones up the systemic fault tolerance ability. According to the standard of "Mining Safety Rules, 2005", we get the estimation factorial weight by the statistic data and expert experience and the training stylebook, looking the monitoring data as the validating stylebook. The results of simulation shows that the method can be used to the assessment of ventilation system, and compares it with the other method based on neural network and D-S evidence theory, the precision is higher