Prediction model for gas outburst intensity of coal mining face based on improved pso and lssvm

Abstract

For the problems of nonlinearity, uncertainty and low prediction accuracy in the gas outburst prediction of coal mining face, the least squares support vector machine (LSSVM) is proposed to establish the prediction model. Firstly, considering the inertia coefficients as global parameters lacks the ability to improve the solution for the traditional particle swarm optimization (PSO), an improved PSO (IPSO) algorithm is introduced to adjust different inertia weights in updating the particle swarm and solve the fitness to stagnate. Secondly, the penalty factor and kernel function parameter of LSSVM are searched automatically, and the regression accuracy and generalization performance is enhanced by applying IPSO. Finally, to verify the proposed prediction model, the model is applied for gas outburst prediction of Jiuli Hill coal mine in Jiaozuo City, and the results are compared with that of PSO-SVM model, IGA-LSSVM model and BP model. The results show that the relative errors of the proposed model are not greater than 2.7%, and the prediction accuracy is higher than other three prediction models. The IPSO-LSSVM model can be used to predict the intensity of gas outburst of coal mining face effectively.