煤矿安全2019，Vol.50Issue(3)：208-211,4.DOI:10.13347/j.cnki.mkaq.2019.03.051
基于GAPSO-SVM的煤层底板破坏程度预测
Prediction of Damage Degree of Coal Seam Floor Based on GAPSO-SVM
摘要
Abstract
In order to correctly predict the damage degree of coal seam floor, the genetic algorithm (GA) and particle swarm optimization (PSO) algorithm have the problems that optimization support vector machine (SVM) is easy to fall into the local optimal solution and the classification accuracy is relatively low. GAPSO-SVM is proposed. The parameters of SVM are optimized by considering the advantages of GA and PSO algorithms. The optimized algorithm can better adjust the balance between the global and local search capabilities of the algorithm. The prediction of the damage degree of the bottom plate of Caozhuang Coal Mine shows that the method can not only achieve good classification effect, but also has higher classification accuracy than GA-SVM and PSO-SVM, and has better robustness.关键词
煤层底板破坏/支持向量机/遗传算法/粒子群优化算法/突水Key words
damage of seam floor/support vector machine/genetic algorithm/particle swarm optimization algorithm/water inrush分类
矿业与冶金引用本文复制引用
..基于GAPSO-SVM的煤层底板破坏程度预测[J].煤矿安全,2019,50(3):208-211,4.
