Parallel, Self Organizing, Consensus Neural Networks

TL;DR

The paper introduces PSCNN, a parallel, self-organizing neural network architecture that enhances performance and speed through input parallelism and consensus decision-making, outperforming traditional backpropagation networks.

Contribution

It presents a novel neural network architecture that combines self-organization, parallel processing, and consensus decision-making, with demonstrated superior performance.

Findings

PSCNN outperforms backpropagation networks in language perception, remote sensing, and binary logic tasks.

The architecture enables fast recall and learning due to its parallel design.

Self-organization of modules maximizes network performance.

Abstract

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.