Title :
The PSO-LSSVM model for predicting the failure depth of coal seam floor
Author_Institution :
State Key Lab. for Geomech. & Deep Underground Eng., Xuzhou, China
Abstract :
Analyzed the samples of the failure depth of coal seam floor collected in mining fields, studied the main influence factors being associated with the failure depth. In order to avoid overfitting problem of artificial neural network (ANN), a new least squares support vector machines (LS-SVM) model is presented to forecast the nonlinear failure depth of coal seam floor under the influence of mining based on particle swarm optimization(PSO) method. PSO is used to choose the parameters of LS-SVM, which can avoid the man-made blindness and enhance the efficiency, even improve the generalization performance. The experimental results show the method is feasible and precise, with reliable theoretical foundation and good practical performance.
Keywords :
coal; generalisation (artificial intelligence); mining industry; neural nets; particle swarm optimisation; support vector machines; ANN; PSO-LSSVM model; artificial neural network; coal seam floor; failure depth prediction; generalization performance improvement; least squares support vector machine model; mining field; nonlinear failure depth forecasting; overfitting problem; particle swarm optimization; Artificial neural networks; Coal; Educational institutions; Floors; Particle swarm optimization; Predictive models; Support vector machines; Failure Depth of Coal Seam Floor; LS-SVM; PSO;
Conference_Titel :
Intelligent Control and Automation (WCICA), 2012 10th World Congress on
Conference_Location :
Beijing
Print_ISBN :
978-1-4673-1397-1
DOI :
10.1109/WCICA.2012.6357944
