ADAPTIVE RESONANCE THEORY-BASED NEURAL-NETWORK FOR SUPERVISED CHEMICAL-PATTERN RECOGNITION (FUZZYARTMAP) .1. THEORY AND NETWORK PROPERTIES

Authors
WIENKE D BUYDENS L
Citation
D. Wienke et L. Buydens, ADAPTIVE RESONANCE THEORY-BASED NEURAL-NETWORK FOR SUPERVISED CHEMICAL-PATTERN RECOGNITION (FUZZYARTMAP) .1. THEORY AND NETWORK PROPERTIES, Chemometrics and intelligent laboratory systems, 32(2), 1996, pp. 151-164
Citations number
30
Categorie Soggetti
Computer Application, Chemistry & Engineering","Instument & Instrumentation","Chemistry Analytical","Computer Science Artificial Intelligence","Robotics & Automatic Control
Journal title
Chemometrics and intelligent laboratory systems → ACNP
ISSN journal
01697439
Volume
32
Issue
2
Year of publication
1996
Pages
151 - 164
Database
ISI
SICI code
0169-7439(1996)32:2<151:ARTNFS>2.0.ZU;2-K
Abstract
The FuzzyARTMAP algorithm is studied with respect to its usefulness fo r supervised chemical pattern recognition. The theory of this relative ly complex artificial neural classifier is presented in detail for che mists. An instructive data set of moderate size, describing male and f emale participants in courses of chemometrics by their body measures, is used to demonstrate how FuzzyARTMAP works and what its basic proper ties are.