中国安全科学学报 ›› 2020, Vol. 30 ›› Issue (5): 39-47.doi: 10.16265/j.cnki.issn1003-3033.2020.05.007
肖鹏1,2 副教授, 谢行俊1,2, 双海清1,2 讲师, 刘朝阳3, 王海宁3, 徐经苍3
收稿日期:
2020-02-05
修回日期:
2020-04-15
出版日期:
2020-05-28
作者简介:
肖鹏(1982—),男,陕西西安人,博士,副教授,主要从事采动覆岩裂隙演化与瓦斯储运及瓦斯高效抽采理论与技术方面的研究。E-mail:xiaopeng@xust.edu.cn。
基金资助:
XIAO Peng1,2, XIE Xingjun1,2, SHUANG Haiqing1,2, LIU Chaoyang3, WANG Haining3, XU Jingcang3
Received:
2020-02-05
Revised:
2020-04-15
Published:
2020-05-28
摘要: 为了精准预测瓦斯涌出量,针对绝对瓦斯涌出量非线性、时变性、复杂性等特点,提出采用核主成分分析法(KPCA)对影响因素进行降维处理;针对BP神经网络(BPNN)中存在的收敛速度慢和易陷入局部最优解的问题,采用压缩映射遗传算法(CMGA)优化BPNN;构建CMGA与BPNN的耦合算法(CMGANN),计算分析某低瓦斯矿井监测历史数据形成的样本集,建立KPCA-CMGANN预测模型;用KPCA-CMGANN预测模型和其他3种网络模型分别对煤矿现场数据进行预测。结果表明:KPCA-CMGANN预测模型在379个时间步长里达到收敛,4个回采工作面的瓦斯涌出量预测相对误差分别为0.58%、0.63%、0.57%和0.45%,平均相对误差仅为0.56%,预测精度和收敛速度均优于对比模型,可实现瓦斯涌出量的快速精准预测。
中图分类号:
肖鹏, 谢行俊, 双海清, 刘朝阳, 王海宁, 徐经苍. 基于KPCA-CMGANN算法的瓦斯涌出量预测研究[J]. 中国安全科学学报, 2020, 30(5): 39-47.
XIAO Peng, XIE Xingjun, SHUANG Haiqing, LIU Chaoyang, WANG Haining, XU Jingcang. Prediction of gas emission quantity based on KPCA-CMGANN algorithm[J]. China Safety Science Journal, 2020, 30(5): 39-47.
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