Adaptive resonance theory based neural network for supervised chemical pattern recognition (FuzzyARTMAP) .1. Theory and network properties

被引：13

作者：

Wienke, D

Buydens, L

机构：

[1] Catholic University of Nijmegen, Laboratory for Analytical Chemistry, 6525 ED Nijmegen

来源：

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS | 1996年 / 32卷 / 02期

关键词：

artificial neural networks; adaptive resonance theory (ART); fuzzy set theory; pattern recognition; CLASSIFICATION; IMPLEMENTATION; ARTMAP;

D O I：

10.1016/0169-7439(95)00084-4

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The FuzzyARTMAP algorithm is studied with respect to its usefulness for supervised chemical pattern recognition. The theory of this relatively complex artificial neural classifier is presented in detail for chemists. An instructive data set of moderate size, describing male and female participants in courses of chemometrics by their body measures, is used to demonstrate how FuzzyARTMAP works and what its basic properties are.

引用

页码：151 / 164

页数：14

共 30 条

[1]

[Anonymous], 1982, STUDIES MIND BRAIN N

[2] ART 2-A - AN ADAPTIVE RESONANCE ALGORITHM FOR RAPID CATEGORY LEARNING AND RECOGNITION [J].