Predicting limit parameters of coal self-ignition based on support vector machine
MENG Qian
WANG Hong-quan
WANG Yong-sheng
ZHOU Yan
Abstract：A SVM ( Support Vector Machine) model for predicting the limit parameters of coal self-ignition was developed. By comparing with polynomial function and Sigmoid function, radial basic function was selected as the kernel function of SVM. A method for optimizing SVM parameters was proposed. The procedure of this method includes three stages: ① Selecting an area large enough to cover the optimal parameters; ② Searching the large area with search step length changing along with the mean squared error of training set, to quickly locate a small area in which the optimal parameters lie; ③ Searching the small area grid by grid for the optimal parameter, which is obviously more efficient than searching the large area directly with grid search method. Experimental results show that, when used for predicting limit parameters of coal self-ignition, SVM-based model performed significantly better than the neural network-based model on both prediction-precision and modeling speed, especially under the condition of limited training samples. So, SVM is feasible for predicting the limit parameters of coal self-ignition.
Keywords：limit parameters of coal self-ignitionsupport vector machineartificial neural networkpredicting model
Publication Date：2009-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 1489-1493 )