基于PSO-LSSVM的矿井冲击地压分级预测研究

Research on classification prediction of mine rock burst based on PSO-LSSVM

为准确预测煤矿冲击地压灾害,提出一种基于粒子群算法(PSO)优化最小二乘支持向量机(LSSVM)预测方法,即冲击地压分级预测的PSO-LSSVM方法。该方法综合考虑煤矿开采深度、地质构造、煤的坚固性系数、最大主应力、煤层倾角变化、煤厚变化、顶板岩层厚度、开采工艺、顶板和底板岩石强度共10项指标因素,构建冲击地压预测指标体系。利用PSO搜索方法对LSSVM模型的核参数σ和惩罚因子f快速寻优,再将优化参数输入LSSVM模型中,构建基于PSO-LSSVM方法的冲击地压危险性分级预测方法,并进行工作面实例预测。研究结果表明:与其他预测方法相比,PSO-LSSVM方法具有计算效率高、准确性高、操作简便等特点,现场应用效果良好。

In order to accurately predict rock burst disasters in coal mines,a prediction method based on particle swarm optimization algorithm(PSO)optimized least squares support vector machine(LSSVM),namely PSO-LSSVM method for rock burst classification prediction,was proposed.In this method,10 indicators including mining depth,geological structure,coal hardness coefficient,maximum principal stress,angle variation of coal seam,variation of coal seam thickness,roof rock thickness,mining technology,rock strength of roof and floor were taken into consideration comprehensively to build the prediction index system of rock burst.The nuclear parameterσand penalty factor f of the LSSVM model were optimized by using PSO search method,and then the optimized parameters were input into the LSSVM model.The classification prediction method of rock burst disasters based on PSO-LSSVM method was established,and the working face was forecasted by it.The results show that compared with other prediction methods,the PSO-LSSVM method has the characteristics of high computational efficiency,high accuracy and simple operation,and the field application effect is good.