Prediction of Coal and Gas Outburst Based on FSVM

Abstract

Coal and gas outburst are one of the natural disasters in coal mines. It is highly destructive and sudden. It is a complex nonlinear problem that is affected by a combination of factors. Fuzzy support vector machine (FSVM) combines the advantages of fuzzy theory and support vector machine (SVM), has strong recognition ability in the case of small samples, and has better learning ability than traditional SVM. In this paper, the gray correlation analysis (GRA) is used to extract coal and gas outburst indicators, an appropriate fuzzy membership function is introduced, and on this basis, a model of coal and gas outburst prediction based on FSVM is proposed. The comparison of verification and other prediction methods proves that the FSVM model can meet the requirements of coal and gas outburst prediction, and the same set of data is trained using FSVM, PSO-SVM and BP neural network system. Experiments prove that FSVM has better prediction accuracy.