机构地区: 中国矿业大学资源与地球科学学院,江苏徐州221116 中国矿业大学煤层气资源与成藏过程教育部重点实验室,江苏徐州221116
出 处: 《煤炭技术》 2017年第9期129-131,共3页
摘 要: 为提高最小二乘支持向量机的预测精度,拓展其应用范围,采用改进后的粒子群优化算法对最小二乘支持向量机进行参数寻优,并应用于芦岭煤矿煤与瓦斯突出危险性类型预测。结果表明优化后的模型比神经网络预测的结果精度高,总体效果良好。 To improve the precision of prediction of least squares support vector machine (LS-SVM), experiment predicts classes of coal and gas outburst in Luling coal mine applying improved PSO to optimize parameters in LS-SVM. Experimental result manifest that prediction accuracy of this model is better than BP neural network and has a great overall effect.