Study on predicted method of mine pressure bump based on improved BP neural network
WANG Yuhong
LIU Lulu
FU Hua
XU Yaosong
Abstract：In order to effectively predict and prevent the mine pressure bump occurred in coal mine,in combination with the acoustic emission technology and the neural network,taking the characteristic parameters of the acoustic emission activity as the basic data,according to the slow convergent speed of the BP neural network,easy in a local extremum and other problems,the BP neural predicted network was improved.The particle swarm optimization algorithm was applied to optimize the BP neural network and the particle swarm algorithm was applied to train the weight value and threshold value of the BP neural network.The results showed that under the condition of the training errors all to be 0.001,in comparison with the not optimized conventional BP neural network,the convergent speed of the particle swarm optimization algorithm neural network would be 4 ～ 5 times fast than not optimized conventional BP neural network.The high convergent speed,high predicted accuracy and other features of the provided prediction method were proved.In the application of the mine pressure bump,the BP neural network was feasible and effective and could provide the theoretical support to the prediction of the mine disaster.
Keywords：mine pressure bumpacoustic emissionneural networkparticle swarm optimized
Publication Date：2017-01-01