Application of Support Vector Machine Model in Mine Gas Safety Level Prediction - Huaping Zhou; Ruixin Zhang (Institute of Advanced Engineering and Science, 2014)
For the limitation of traditional information fusion technology in the mine gas safety class predicition, an intelligent algorithm is proposed in which Genetic Algorithms is adopted to optimize the parameters of the least squares support vector machine and establishes a multi-sensor information fusion model GA-LSSVM which overcomes the subjectivity and blindness on parameters selection, and thus improves its classification accuracy and convergence speed. Experimental results show that compared to the least squares support vector machine model not been optimized and the least squares support vector machine model optimized by the grid searching algorithm, GA-LSSVM model can be a good solution on the issue of the high-dimensional, nonlinear and uncertainty of the small sample in coal mine underground environment level evaluation. DOI : http://dx.doi.org/10.11591/telkomnika.v12i5.4364