基于神经网络的矿用红外瓦斯传感器检测模型的研究

摘要

The paper briefly introduced the principle of gas infrared detection and pointed out existing problems of traditional absorption model. It established a detection model of infrared gas sensor based on nonlinear approximation capability of RBF neural network, gave the structure of the RBF neural network, and trained the RBF neural network, then obtained RBF neural network structure of the detection model of infrared gas sensor. The experiment results showed that the model has small error and high accurate, which can meet the requirements of mine application.%文章简要介绍了瓦斯红外检测原理,指出了传统吸收型模型的不足,基于RBF神经网络的非线性逼近能力建立了一种红外瓦斯传感器检测模型,给出了RBF神经网络的组织,并对RBF神经网络进行了训练,得到了红外瓦斯传感器检测模型的RBF神经网络结构.实验结果表明,该模型误差小、精度高,可满足煤矿井下应用的需要.