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
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.