中文摘要：

差分吸收光谱（DOAS）法是一种有效的监测大气污染气体浓度的光学遥感方法,不仅有好的时间分辨率,而且测量灵敏度也很高。但是由于遥测系统处于复杂的大气环境中,各种干扰因素以及恶劣的气候条件,都会对系统产生影响。针对现有实时、在线监测差分吸收光谱系统中存在的不足,作者提出了一种基于改进Elman网络的实时预测模型。利用逐步回归筛选预测因子,不仅降低了预测网络的复杂程度,而且增强了系统的预测实时性。利用带自适应学习率的动态BP算法对改进的Elman网络进行训练,使预测系统能更好地辨识要预测的差分吸收光谱系统,该模型能较准确地对DOAS系统监测污染物数据进行实时跟踪监控,一定程度上弥补了遥测系统的不足。

英文摘要：

For real-time and on-line monitoring DOAS （differential optical absorption spectroscopy） system,a model based on an improved Elman network for monitoring pollutant concentrations was proposed. In order to reduce the systematical complexity,the forecasting factors have been obtained based on the step-wise regression method. The forecasting factors were current concentrations,temperature and relative humidity,and wind speed and wind direction. The dynamic back propagation （BP） algorithm was used for creating training set. The experiment results show that the predicted value follows the real well. So the modified Elman network can meet the demand of DOAS system＇s real time forecasting.