Adaptive Resonance Theory (ART) is a kind of neural network
based on neurophysiologic theories.
Stephen Grossberg came up with the idea in 1976.
For prediction, ART models use a hidden layer of ideal
instances.
If an input case is sufficiently similar to an existing
case, it "resonates" with it, and the ideal case is modified to
include it.
A new ideal scenario is introduced if this is not the case.
ARTs are sometimes shown as having two layers, known as the
F1 and F2 layers.
The matching is done by the F1 layer, and the outcome is
chosen by the F2 layer.
It's a cluster analysis technique.
Internet References:
http://www.wi.leidenuniv.nl/art/
ftp:://ftp.sas.com/pub/neural/FAQ2.html
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