基于BP神经网络的红外可燃气体探测器温度补偿研究

Study on temperature compensation of infrared combustible gas detector based on BP neural network

针对红外可燃气体探测器输出电压随温度变化,而又缺乏理论补偿公式的情况,提出采用BP神经网络对基于NDIR的可燃气体探测器进行温度补偿的方法,解决了以往温度补偿通过硬件补偿带来的探测器体积大、重量增加的弱点;与已有的软件补偿方法相比,BP神经网络方法通过对数据进行训练、学习获得温度补偿模型,得到了较好的结果,使得红外可燃气体探测器具有较宽的使用温度范围。

The output voltage of infrared combustible gas detector varies with temperature, however, there is no theoretical formula which can be used to compensate this influence. In this paper, a temperature compensation method is proposed for NDIR combustible gas detector, based on the BP neural network. Compared with the existing software compensation methods, the new method can use data training and learning to achieve better temperature compensation models, which can realize a wider usage temperature range of the infrared combustible gas detector.