The Research on Gas Outburst Danger Prediction Model Based on PSO-SVM

Coal and gas outburst accident endangers miners' lives and damages production site. If the relevant data of gas outburst is monitored, the danger degree of gas outburst is predicted, the property and life loss caused by gas outburst hazard can reduce greatly. Therefore, this paper propose a gas outburst danger prediction model based on combination of support vector machine (SVM) algorithm and improved particle swarm optimization (IPSO) algorithm. Firstly, the optimal parameters for SVM is solved by improved particle swarm optimization (IPSO) algorithm, which has better inspiration performance and relapses into local optimal solution less. Secondly, the solved optimal parameters are used by SVM algorithm to train sample data for data classification, because SVM algorithm is good at pattern recognition. At last, gas outburst danger prediction model has built up. The experimental results show that the method adopted to improve the accuracy of fault detection by 5%.