利用人工神经网络方法提高差分光学吸收光谱系统测量精度研究

差分光学吸收光谱法已经变成了测量大气中微量气体浓度常用的方法。微量气体的浓度通过对大气吸收光谱的分析得到。但在实际应用中，由于受到硬件条件的限制，使得每次分析的光谱带宽有限，造成分析的误差较大，结果不够稳定。这里提出了一种利用多层自适应线性（Madaline）人工神经网络对光谱进行扩展的方法，并对试验结果进行了比较，收到了良好的效果。

Improving DOAS System Measurement Precision with Artificial Neutral Network Method

Differential optical absorption spectroscopy(DOAS) has become a widely used method to measure trace gases in the atmosphere. Their concentrations are retrieved by a numerical analysis of the atmospheric absorption spectra. But in the process of application, it is found the error is a bit larger and results are not steady since the limited bandwidth for each analysis subject to system hardware. A new procedure was developed, based on the Madaline artificial neural network theory to expand the spectra bandwidth. A good result was obtaind with this method according to the comparison of the different test results.