Class-modeling using Kohonen artificial neural networks

被引:44
作者
Marini, F
Zupan, J
Magrì, AL
机构
[1] Univ Roma La Sapienza, Dipartimento Chim, I-00185 Rome, Italy
[2] Natl Inst Chem, SLO-1000 Ljubljana, Slovenia
关键词
class-modeling; Kohonen self-organizing maps; artificial neural networks; chemometrics; pattern recognition;
D O I
10.1016/j.aca.2004.12.026
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
In this paper, a class-modeling technique based on Kohonen artificial neural networks is presented. In particular, in order for the Kohonen self-organizing map to operate as a class-modeling device, two main issues are identified: integrating the training set (composed of samples from a single category) with a set of uniformly distributed random vectors and computing a suitable probability distribution associated to the positions on the 2D layer of neurons. Both the identified features concur in defining an opportune class space. When used to analyze a real-world data set (classification of rice varieties), the proposed technique provided comparable and in some cases better results than the traditional chemometric techniques SIMCA and UNEQ. (c) 2004 Elsevier B.V. All rights reserved.
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页码:306 / 314
页数:9
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