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

为了对煤层底板破坏程度进行正确预测,分析遗传算法(GA)和粒子群优化(PSO)算法存在优化支持向量机(SVM)易陷入局部最优解和分类精度相对较低的问题,提出了GAPSOSVM优化算法。综合考虑GA和PSO算法的优点对SVM的参数进行了优化,优化后的算法能够较好地调整算法的全局与局部搜索能力之间的平衡。通过对曹庄煤矿底板破坏程度的预测表明,该方法不仅能够取得良好的分类效果,分类精度高于GA-SVM和PSO-SVM,而且有较好的鲁棒性。

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.