Research on Application of Improved Gray Neural Network in Fire Forecasting
Hao Xu · Zhongguo anquan kexue xuebao · 2012
In order to improve the prediction accuracy of fire and reduce fire loss,the structural defects concerning background values in the traditional GM(1,1) model were discussed and tackled.The improved gray model and the BP neural network model were integrated to present the improved gray neural network prediction model for fire accidents.According to the statistical data of 1997-2009 fire accidents,the improved GM(1,1) model and the improved gray neural network model were respectively used for fitting simulation of the 1997-2007 fire disaster occurrence number,the prediction results of 2008-2009 fire disaster occurrence number were obtained.The results show that this new model avoids the structural defects concerning back ground values in the GM(1,1) model,and has the advantages of both the gray system and the neural network.Moreover,it not only embodies the complex gray system behavior of fire and could adaptively adjust the learning rate.Compared with the single GM(1,1) model,the predicted result of this new model owns the higher precision.