Abstract :
In this paper I describe the use of neural
network in various related fields. Artificial neural
networks are parallel computational models, comprised of
densely interconnected adaptive processing units. These
networks are fine-grained parallel implementations of
nonlinear static or dynamic systems. A very important
feature of these networks is their adaptive nature where
"learning by example" replaces "programming" in solving
problems. This feature makes such computational models
very appealing in application domains where one has little
or incomplete understanding of the problem to be solved,
but where training data is available. Another key feature is
the intrinsic parallel architecture which allows for fast
computation of solutions when these networks are
implemented on parallel digital computers or, ultimately,
when implemented in customized hardware.
Keyword :
CATCH, MJ Futures, Image Compression, SOM.