基于BP神经网络数据融合的瓦斯监测系统

井下瓦斯监测系统为多传感器监测系统,它通过不同功能、不同精度、不同位置的传感器,对所需要的被测量进行多方位、多角度的测量。但是,目前对于多传感器所测的数据还没有一种通用的、行之有效的处理方法,井下瓦斯浓度的监测很难作到实时、精确。因此,文章提出了一种基于BP神经网络数据融合的瓦斯监测系统的设计方案,该方案采用改进的BP神经网络算法对多传感器数据进行融合,并采用两级融合的方式对数据进行处理,以得到井下环境特征。仿真结果表明,基于BP神经网络数据融合的瓦斯监测系统具有较高的测量精度,极大地提高了数据采集的可靠性、全面性和有效性。

Gas Monitoring System Based on Data Fusion with BP Neural Network

Mine gas monitoring system is a multi-sensor monitoring system,which measures circumstance from many azimuthes using sensors with different function,different precision and different position.But at present,there is no general and effective method to process multi-sensor data,as the result real-time and precision of mine gas monitoring system is hard to reach.So,the paper put forward a design scheme of gas monitoring system based on data fusion with BP neural network.The scheme used improved BP neural network algorithm to fuse multi-sensor data,and used two-stage fusing mode to process the data,which can gain environmental characteristics of coal mine underground.The simulation result showed that the gas monitoring system based on data fusion with BP neural network has higher measuring precision,and greatly improves reliability,integrality and efficiency of data collection.