Mine Intelligent Fire Disaster Detection Method Based on Radial Basis Function Neural Network

In order to improve the environment suitability and the anti interferences capacity of the mine fire disaster detector, the radial basis function neural netw ork with the approximation capacity, classification capacity and learning speed better than the BP network was applied to establish the mine fire disaster detection simul ation model under the MATLAB environment. The temperature, smoke density and CO density was applied to the input for the multi information data integration to reach the target of the mine fire disaster detection.The simulation results showed that the identification probability error of the open fire, the shade fire and no fire by the meth od would be all less than 5% and the method could highly reduce the missed detection and incorrect detection rate of the fire disaster early warning.The means combin ed with the fuzzy system and the neural network could effectively monitor and measure the mine fire disaster to be occurred and would have the reference value to the study on the intelligent fire disaster warning system.