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。
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.
出处
《中国安全科学学报》
CAS
CSCD
北大核心
2013年第5期56-61,共6页
China Safety Science Journal
基金
国家自然科学基金资助(51274118
70971059)
辽宁省教育厅基金资助(L2012119)
辽宁省科技攻关项目(2011229011)
