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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。
In order to improve the prediction precision of LS-SVM and extend its application range, the improved Particle Swarm Optimization (PSO) algorithm is used to optimize the parameters of LS-SVM and applied to the coal and gas outburst hazard types in Luling Mine prediction. The results show that the optimized model is more accurate than the neural network prediction and the overall effect is good.