Gas concentration prediction model for fully mechanized coal mining face

In view of problems of gas concentration prediction method based on least squares support vector machine (LS-SVM) such as easy to fall into local optimal solution, low search efficiency and easy to occur premature convergence during parameter optimization process, a gas concentration prediction model based on ACO-LS-SVM was proposed. Firstly, k-means clustering analysis is performed on collected large amount of gas data on fully mechanized coal mining face to reduce dimension. Then, improved ant colony algorithm is used to optimize penalty parameters and kernel function parameters of LS-SVM, and the optimized parameters are substituted into the LS-SVM model for regression prediction. The simulation results show that when absolute error threshold of gas concentration is 0.03%, 0.04%, 0.05%, the prediction accuracy of the gas concentration prediction model based on ACO-LS-SVM is about 95%, which is better than SVM model and LS-SVM model.