摘要
利用人工神经网络(ANN)对严重混叠的傅里叶变换红外光谱图进行了定性和定量解析。通过大量模拟数据训练神经网络后,引用了新的评价标准———逼近度来选择最优网络模型。利用此优化网络对两类光谱图进行了解析,考察了网络的泛化能力。结果表明:该网络不仅能够对两组分同时存在时的样本进行准确解析,而且对于未知单组分光谱图,也能够进行准确鉴别和定量分析。可见,该研究为人工神经网络在单组分和多组分未知物的定性和定量分析方面提供了一种新思路。
Quantitative analysis of FTIR spectra, which are seriously overlapped in the spectral bands, was studied by artificial neural networks. The optimum network was chosen by a new criterion, i.e. the degree of approximation. After the network was established, two kinds of spectra were re.solved. It was demonstrated that accurate results could be obtained when two components were both included. In addition, the unknown spectrum could be identified and quantified. It was showed that the artificial neural network has excellent non-linear ability of solution. Meanwhile, the method provides an efficient approach to the identification and quantification of the unknown samples.
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2006年第1期51-53,共3页
Spectroscopy and Spectral Analysis
基金
国家自然科学基金(20175008)
中国博士后科学基金
南京理工大学青年学者基金(Njust200303)资助项目
