A FUZZY ADAPTIVE RESONANCE THEORY SUPERVISED PREDICTIVE MAPPING NEURAL-NETWORK APPLIED TO THE CLASSIFICATION OF MULTIVARIATE CHEMICAL-DATA

Citation
Xh. Song et al., A FUZZY ADAPTIVE RESONANCE THEORY SUPERVISED PREDICTIVE MAPPING NEURAL-NETWORK APPLIED TO THE CLASSIFICATION OF MULTIVARIATE CHEMICAL-DATA, Chemometrics and intelligent laboratory systems, 41(2), 1998, pp. 161-170
Citations number
24
Categorie Soggetti
Computer Science Artificial Intelligence","Robotics & Automatic Control","Instument & Instrumentation","Chemistry Analytical","Computer Science Artificial Intelligence","Robotics & Automatic Control
ISSN journal
01697439
Volume
41
Issue
2
Year of publication
1998
Pages
161 - 170
Database
ISI
SICI code
0169-7439(1998)41:2<161:AFARTS>2.0.ZU;2-1
Abstract
A fuzzy adaptive resonance theory-supervised predictive mapping (Fuzzy ARTMAP) neural network has been studied for the classification of mul tivariate chemical data. Fuzzy ARTMAP achieves a synthesis of fuzzy lo gic and adaptive resonance theory (ART) by exploiting the close formal similarity between the computations of fuzzy subset membership and AR T category choice, resonance, and learning. To examine the properties of Fuzzy ARTMAP, the well-known Italian olive oil data set was employe d. Then this method was applied to a practical agricultural data set t o classify different soil samples depending on the crops grown on them . For comparison, the back-propagation (BP) neural network has also-be en used to treat these data. The results show that the classification performance of the Fuzzy ARTMAP neural network is as good or better th an the BP network in the present applications. Among other features, t he Fuzzy ARTMAP needs less training time and fewer algorithmic paramet ers to be optimized than BP does to achieve good classification. (C) 1 998 Elsevier Science B.V. All rights reserved.