LAUSR

Parameter selection is a key factor affecting the performance of support vector machines (SVMs). To further improve the classification accuracy and generalization ability of SVMs, a parameter selection model for SVMs with RBF kernel is proposed based on adaptive differential evolution (ADE) algorithm, and is applied to predict coal and gas outbursts. The function of each parameter and its adjustment scheme of differential evolution (DE) algorithm are analyzed, and the algorithm is improved by using the decision error rate of samples as the objective function. Adaptive calculation equations for variability factor and crossover factor are designed. The variability and crossover factors are automatically adjusted in the execution process of the algorithm, so that the population diversity is maintained in the early stages of algorithm execution to enhance the ability of searching global optimal values, while the stability of the algorithm is guaranteed in the late stages by promoting the searching ability for local optimal values. A novel ADESVM model for predicting coal and gas outbursts is established by using ADE algorithm to select SVM parameters, which is applied to predict the coal and gas outbursts. Experimental results show that the designed ADE algorithm has high convergence speed and high computational accuracy. The proposed ADESVM model has higher training speed and is more robust compared with other similar SVM models. It also has higher prediction accuracy and shorter training time, compared with back propagation neural networks, providing a new method for the intelligent prediction of coal and gas outbursts.