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中文摘要：
为有效预防瓦斯灾害,以预测矿井瓦斯涌出量为研究目的,提出经改进的粒子群算法（MPSO）优化的加权最小二乘支持向量机（WLS-SVM）,并用其预测非线性动态瓦斯涌出量。算法通过对WLS-SVM的正则化参数C和高斯核参数σ寻优,建立基于MPSO优化的WLS-SVM的瓦斯涌出量预测模型,并利用某矿井监测到的各项历史数据进行实例分析。试验结果表明：该预测模型预测的最大相对误差为5.99%,最小相对误差为0.43%,平均相对误差为2.95%,较其他预测模型有更强的泛化能力和更高的预测精度。
英文摘要：
In order to prevent gas disasters effectively,to predict mine gas emission,a WLS-SVM optimized with MPSO algorithm was worked out.The regularization parameter C and the gaussian kernel parameter σ of WLS-SVM were optimized with the MPSO-WLS-SVM algorithm.A MPSO-WLS-SVM-based model was built for gas emission quantity prediction.The model was validated by using the historical data on a certain coal mine in China.The results show that the maximum relative error is 5.99%,the minimum relative error is 0.43% and the average relative error of results predicted by the model is 2.95%,and that this model has stronger generative capacity and higher prediction accuracy than the other models.
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