Elman回归神经网络同时定量测定三种酚类化合物

应用Elman回归神经网络(ERNN)对光谱严重重叠的对-硝基苯酚,邻-硝基苯酚和2,4-二硝基苯酚体系的同时定量测定进行了研究,并与多变量线性回归(MLR)法作了比较.编制了PERNN和PMLR程序执行有关计算.通过最佳化确定了Elman回归网络的结构和参数.ERNN和MLR法所有组分的相对预测标准偏差(RSEP)分别为3.1%和2 027.3%,实验结果显示对于分辨严重重叠光谱本法是成功的.ERNN法是解决局部最小和提高收敛速度的一种有价值的工具,亦可用于分析全光谱而不只限于选取少数特征值.本法为不经预先分离同时测定严重重叠的分子光谱体系提供了新的途径.

Simultaneous Quantitative Analysis of Three Kinds of Phenols by Elman Recurrent Neural Network

Elman recurrent neural network ( ERNN) was applied to study the simultaneous quantitative analysis of seriously overlapped spectra of a p-nitrophenol, o-nitrophenol and 2,4-dinitrophenol system. The multivariate linear regression (MLR) method was also applied in this study for comparison. Two programs (PERNN and PMLR) were designed to perform the calculations. By optimization, the structure and parameters of Elman recurrent neural network were defined. The relative standard errors of prediction (RSEP) for all components with ERNN and MLR were 3.1% and 2 027.3%, respectively. Experimental results showed the method to be successful even where there was a severe overlap of spectra. The ERNN method is a valuable tool in solving the minimum problem and improving the convergence rate, and can be used to analyze the whole spectra rather than just picking out a few characteristic values. The method provides a new way of simultaneous determination of severely overlapped molecular spectra without prior separation.