FEATURE-EXTRACTION OF POLYSACCHARIDES BY LOW-DIMENSIONAL INTERNAL REPRESENTATION NEURAL NETWORKS AND INFRARED-SPECTROSCOPY

被引：12

作者：

JACOBSSON, SP

机构：

[1] Kabi Pharmacia Therapeutics

来源：

ANALYTICA CHIMICA ACTA | 1994年 / 291卷 / 1-2期

关键词：

INFRARED SPECTROMETRY; FEATURE EXTRACTION; MULTIVARIATE ANALYSIS; NEURAL NETWORKS; POLYSACCHARIDES; PRINCIPAL COMPONENT ANALYSIS;

D O I：

10.1016/0003-2670(94)85123-9

中图分类号：

O65 [分析化学];

学科分类号：

070302 ; 081704 ;

摘要：

A new method for exploratory data analysis of spectroscopic data by neural networks is described. The method is based on the weight distribution associated with objects in narrow layered neural networks in which the input spectra are identical to the output spectra. The objects are displayed in 2- or 3-dimensional plots in analogy to principal component plots. The information content of the plots generated by low-dimensional internal representation neural networks, in their non-linear mode, appears to be at least as good as that of principal component analysis.

引用

页码：19 / 27

页数：9

共 9 条

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