Parallel, Self Organizing, Consensus Neural Networks
This work addresses the need for faster and more efficient neural networks, particularly for applications in language perception and remote sensing, though it appears incremental as it builds on existing self-organizing models.
The authors introduced PSCNN, a neural network architecture that enhances performance and speed through parallelism, self-organization, and consensus-based decision-making, showing superior results compared to Backpropagation networks in tasks like language perception, remote sensing, and binary logic.
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.