Abstract：According to the coupling interaction characteristics between the coal seam gas content and its influence factors, BP neural network prediction method based on particle swarm optimization algorithm is proposed for the limitations of BP neural network in coal seam gas content prediction such as slow convergence rate and poor reliability, which organically combines the BP neural network and particle swarm optimization algorithm.On the basis of predicting coal seam gas content with BP neural network, particle swarm optimization algorithm is applied to optimize the weights and thresholds of the network. According to the measured data, a particle swarm neural network training and testing samples are established to train and check the prediction model. The simulation results indicate that the adopted prediction model has the characteristic of high reliability and prediction precision, which speeds up the network convergence rate and overcomes the local optimum of a BP neural network.

Keyword：gas content;particle swarm optimization;neural network;prediction model;