Design new supervised art-type artificial neural networks, and their performances for classification landsat TM images

Abstract

New Supervised ART ANNs with simple architectures have been developed in
this study. Their architectures have been built from a single module of ART rather than
a pair of them connected by a map field as all other supervised ART-type ANNs that
have been reported in the literatee. Two different algorithms have been developed:
Supervised ART-I and Supervised ART-II. The developed algorithms reduced the
number of dynamic parameters, memory requirement, and the training time which is the
major problem facing the ANNs, without altering the classification accuracy.
Two simplified versión of Fuzzy ART algorithms have been developed, keeping
the categorization performance as that of the original algorithm. They are Flagged
Fuzzy ART and Compact Fuzzy ART. While Supervised ART-I and Supervised ART-II
are general in nature that can be applied to all ART ANNs, the supervisión of Compact
Fuzzy ART has been addressed in this work. The full algorithms for Supervised ART-I
and Supervised ART-II have been listed.
The newly developed ANNs have been applied to classify Landsat Thematic
Mapper (TM) images. The performance of the systems has been tested for different
dynamic parameters and different training samples. The behavior of the systems in the
vigilance parameter and dynamic learning parameter space has been addressed. Their
performances in the domain of the vigilance parameter and the dynamic learning
parameter have been understood.
Only one approach, for vigilance dynamic in all supervised ART-type ANNs,
has been addressed in the literatee. Three more approaches have been developed in this
study: fixed, free, and float. The performance of the developed ANNs for classification
landsat TM images has been tested for all these different vigilance dynamics.