摘要
为了从含有噪声的大气红外光谱中提取微量待测污染组分的定量特征 ,进而建立校正模型 ,本文提出了一种基于统计理论的波长选择方法。该方法针对待测组分 ,在对光谱各波长位置的噪声强度进行统计估计的基础上 ,提出了选择最佳波长子集的目标函数。该目标函数包含波长子集的噪声和长度参数 ,这使得在最小化模型误差的同时也防止了模型规模的无限制膨胀。为了检验该方法的性能 ,文章利用含背景噪声的实测光谱数据针对三种气体进行了波长选择 ,并利用神经网络技术分别建立了校正模型。实验结果与波长子集的优化结果相符 ,所选择的波长子集的长度不足光谱波长总点数的 2 % ,同时 ,光谱中的噪声也在校正模型中得到了明显的抑制。实验结果证明了该波长选择方法的有效性。
In order to extract the quantitative features of the rare pollution components from noisy atmospheric infrared spectra and thus create calibration models, a wavelength selection method based on statistic theory is proposed in this paper. In this method, an objective function is defined based on the estimation of spectral noise level at every wavelength position. Because the size of the wavelength subset is also included in the function, the model size will not become too big during the minimization of the error of the calibration model. To test the performance of this method, the wavelength subsets of measured spectra with background noises were selected and the calibration models were then created using neural network technique for three pollution gases, respectively. The test showed that the sizes. of the selected wavelength subsets accorded with the calculated results. The subset sizes were less than 2% of the total wavelength points. Meantime, the spectral noises were also restrained markedly in the calibration model because of the wavelength selection. The experimental results proved the validity of the wavelength selection method.
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2001年第5期599-602,共4页
Spectroscopy and Spectral Analysis
基金
清华大学博士论文基金 (No 980 7)资助
