参数优化LSSVM的巷道围岩松动圈预测研究

A predicative study of loosening zones around roadways with least square support vector machines method with optimized parameters

最小二乘支持向量机方法(LSSVM)在处理小样本、高维数、非线性的问题时,具有求解速度快、易于描述非线性关系的优良特性。但是,该方法得到的模型拟合精度和泛化能力依赖于其相关参数,因此,提出基于粒子群优化算法(PSO)的LSSVM参数优选方法。最后,用该模型对巷道围岩松动圈进行了预测研究。结果表明,PSO优化的LSSVM模型具有收敛速度快、计算精度高的特点,说明该模型是合理、有效的。

Least square vector machines had the quickly solving speed and the excellent characteristics to describe the little samples,nonlinear and high dimensions problem. But the regression accuracy and generalization performance of this method depend on a proper setting of its parameters. So,an optimal selection approach of LSSVM was presented based on particle swarm optimization(PSO) algorithm. Then,the prediction on the thickness of the loosen zone around roadway is made by PSO-LSSVM model,and the results show that this model has the characteristic of quickly convergence speed and highly calculating precision .So,this model is reasonable and feasible.