Prediction of coal and gas outburst intensity based on LLE-FOA-BP model

In view of problems of low prediction accuracy, poor stability and slow training of current coal and gas outburst intensity prediction, a prediction method of coal and gas outburst intensity based on LLE-FOA-BP model was proposed. Essential characteristics of raw data of coal and gas outburst influencing factors are extracted taking use of the advantage of nonlinear data feature extraction of LLE algorithm, effective reconstruction factors are formed, and redundant information and noise between data are reduced. The weight and threshold of BP neural network are optimized by using FOA algorithm's strong global optimization ability to avoid falling into local minima and improve parameter optimization efficiency. Effective reconstruction factors are input into the optimized BP neural network for training, so as to realize quick and accurate prediction of coal and gas outburst intensity.The test results show that the average relative error of LLE-FOA-BP model is 8.06%, variance of relative error is 3.69, and training accuracy of 10-8 is achieved after 24 iterations, which verifies the model can improve robustness and learning efficiency while ensuring prediction accuracy.