在信息融合技术下的煤矿底板突水预测方法研究  

Study on Prediction Method of Coal Mine Water Inrush in Information Fusion Technology

作　　者：胥良[1] 梁亚[1] 郭林[1] 

机构地区：[1]黑龙江科技大学电气与控制工程学院,哈尔滨150022

出　　处：《煤炭技术》2015年第4期190-192,共3页Coal Technology

摘　　要：分析煤矿工作面突水有关的水文、地质环境、采矿影响等诸多因素,发挥RBF神经网络的自学习、自适应和容错能力,将其运用到不同突水因素的基本概率分配的确定过程中,建立基于RBF神经网络和D-S证据理论的煤矿底板突水预测模型。通过MATLAB仿真验证了该模型的预测结果准确度,保证煤矿的安全生产。Analysis on factors of coal mine working face water inrush related hydrological, geological environment, mining influence, exert the self learning, adaptive and fault-tolerant ability of RBF neural network, and applies it to the process of determining the basic probability assignment of the different factors of water bursting in, establish the prediction model of mine water inrush RBF neural network and D-S evidence theory based on. Through MATLAB simulation to verify the predictions of the model accuracy, and ensure safety production in coal mine.

关 键 词：RBF神经网络 D-S证据理论 突水预测 

分 类 号：TD745[矿业工程—矿井通风与安全]