  A new neural network architecture (PSCNN) is developed to improve performance
and speed of such networks. The architecture has all the advantages of the
previous models such as self-organization and possesses some other superior
characteristics such as input parallelism and decision making based on
consensus. Due to the properties of this network, it was studied with respect
to implementation on a Parallel Processor (Ncube Machine) as well as a regular
sequential machine. The architecture self organizes its own modules in a way to
maximize performance. Since it is completely parallel, both recall and learning
procedures are very fast. The performance of the network was compared to the
Backpropagation networks in problems of language perception, remote sensing and
binary logic (Exclusive-Or). PSCNN showed superior performance in all cases
studied.
