基于BP神经网络的矿井一氧化碳检测方法研究

文章采用热催化传感器和电化学式气体传感器的配合使用，为了解决两种传感器对矿井一氧化碳和甲烷气体的“交叉敏感”问题，提出了一种基于BP神经网络技术的传感器系统。研究了采用两种传感器组成的多传感器阵列与BP神经网络相结合来实现一氧化碳气体浓度精确检测的方法。实验证明，利用基于BP神经网络的多传感器阵列模型，能有效的提高对井下一氧化碳气体的测量精度。

Research on Detection of CO in Mine Based on BP Neural Network

Because of the effect of methane gas to electrochemical sensor, precision of CO detection was reduced. In order to increase the precision of CO detection, electrochemical sensor was used with a catalytic sensor in this paper. Because there was the cross sensitivity of two sensors to CO and methane gas in mine, a gas sensor array system based on BP neural network and the method of signal processing on BP neural network for increasing precision of detection CO were researched in this paper. The experiment showed that the gas sensor array based on BP neural network could increase the precision of detection CO.