基于GA-SVM的矿井涌水量预测OA北大核心CSCDCSTPCD
Mine water inflow prediction based on GA-SVM
矿井涌水量的准确预测对预防矿山透水事故的发生至关重要,提出利用GA优化的SVM模型(GA-SVM)来实现矿井涌水量的短期准确预测.该方法利用GA的自动寻优功能寻找SVM的最佳参数,提高了预测的准确率.首先,利用微熵率法求矿井涌水量时间序列的最佳嵌入维数和延迟时间,进行相空间重构.其次,采集义煤集团千秋煤矿2011-2015年实际涌水量的时间序列,利用GA-SVM模型对最后12组数据进行预测,其预测平均绝对百分比误差仅为0.92％,最大相对误差为2…查看全部>>
The accurate prediction of mine water inflow is very important to prevent mine water inrush accident.In this paper,the SVM model optimized by GA(GA-SVM) was put forward to realize the short term and accurate prediction of mine water inflow.In this method the automatic optimization function of GA was used to find the optimal parameters of SVM,which can improve the accuracy of prediction.Firstly,the optimal embedding dimension and delay time of mine water infl…查看全部>>
乔美英;程鹏飞;刘震震
河南理工大学电气工程与自动化学院,河南焦作454000煤炭安全生产河南省协同创新中心,河南焦作454000河南理工大学电气工程与自动化学院,河南焦作454000
矿业与冶金
矿井涌水量混沌时间序列相空间重构GA-SVM
mine water inflowchaotic time seriesphase space reconstructionGA-SVM
《煤田地质与勘探》 2017 (6)
量化集值系统迭代学习控制理论及应用
117-122,6
国家自然科学基金项目(61573129,51474096)河南省教育厅重点科研项目(16A120004,16A440007)National Natural Science Foundation of China(61573129,51474096)Foundation of Henan Educational Committee(16A120004,16A440007)
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