基于多传感器数据融合的煤矿安全预测  

Coal Mine Safety Forecasting Based on Multisensor Data Fusion

作　　者：李振华[1] 蒋涛[1] 

机构地区：[1]安徽理工大学电气与信息工程学院,安徽淮南232001

出　　处：《煤矿机械》2015年第5期257-259,共3页Coal Mine Machinery

摘　　要：为了解决煤矿井下环境信息处理的延迟性和不确定性,提出了一种基于多传感器数据融合的煤矿安全预测模型。该模型为两级融合模型,一级模型基于D-S合成算法,解决了单一传感器在井下复杂环境下测量的不确定性和失效性以及数据处理速度缓慢问题。二级模型基于模糊神经网络,解决了井下精确数学模型难以建立问题同时具有自适应和自学习能力。实验表明:该二级模型实现了多个传感器信息的融合,一定程度上提高了预测准确度和数据处理速度。In order to solve the coal mine environment information processing delay and uncertainty, this paper puts forward a prediction model of coal mine safety based on muhisensor data fusion.The model for the two-level fusion model, primary model based on D-S synthesis algorithm, to solve the single sensor measurement in underground complex environment uncertainty and failure and the data processing speed slow problem. Secondary model based on fuzzy neural network, solve the problem of the underground is difficult to establish accurate mathematical model of both adaptive and self-learning ability. Experiments show that the secondary model realized the multiple sensor information fusion, a certain extent, improve the prediction accuracy and real-time data processing speed.

关 键 词：D-S合成法 数据融合 模糊神经网络 算法 安全预测 

分 类 号：TD76[矿业工程—矿井通风与安全]