Elman回归神经网络同时定量测定三种酚类化合物

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Abstract 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.

Key words：Elman recurrent neural network;Simultaneous quantitative analysis;Overlapped spectra;Multivariate linear regression

Received: 2004-12-18 Accepted: 2005-05-20

ZTFLH:

O657.3

Corresponding Authors: REN Shou-xin

Cite this article:

GAO Ling,SHI Jun-xian,REN Shou-xin. Simultaneous Quantitative Analysis of Three Kinds of Phenols by Elman Recurrent Neural Network [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2006, 26(01): 117-120.

URL:

https://www.gpxygpfx.com/EN/Y2006/V26/I01/117