基于神经网络校正算法的酒精非接触测量方法

为了解决酒精气体测量过程中其他外界因素对测量浓度影响的问题，本文结合酒精气体在红外谱段吸收的特性以及BP神经网络算法的非线性处理方法提出了一种基于神经网络校正算法的酒精气体非接触测量方法。该算法考虑气体吸收过程中温度、湿度对光强的影响，把其作为神经网络的输入和测量参数一起进行训练，同时与常规的数据拟合模型算法进行对比实验，实验证明该算法取得了较好的效果。

A Non-contact Alcohol Measurement Method Based on Neural Network Correction Algorithm

This paper presents a non-contact method for the measurement of alcohol gas emission based on the neural network correction algorithm, to mitigate the influence of external factors on the measurement process. The proposed method combines the characteristics of alcohol gas absorption in the infrared spectrum and the nonlinear processing method of the back propagation(BP) neural network algorithm. The algorithm considers the influence of temperature and humidity on light intensity during the gas absorption process and trains it as the input to the neural network and measurement parameters. Simultaneously, the proposed algorithm is compared with the data fitting algorithm, and the experimental results show that this algorithm achieves better results.