Parallel, self organizing, consensus neural networks
Homayoun Valafar, Faramarz Valafar, Okan K. Ersoy · 2003
A neural network architecture, the parallel self-organizing consensus neural net (PSCNN), is developed to improve performance and speed of such networks. The architecture has all the advantages of previous models such as self-organization and possesses new or superior characteristics such as input parallelism and decision making based on consensus. Due to the parallel properties of this network its parallel implementation on an N-cube machine was also studied. The architecture self organizes its modules to maximize performance. Since the system is completely parallel, both recall and learning procedures are very fast. The performance of the network was compared to backpropagation networks in problems of language perception remote sensing and binary logic (Exclusive-Or). PSCNN showed superior performance in all cases studied. In the research reported in the paper, we demonstrate and test the development of the PSCNN's architecture as well as its training rules. In addition, the performance of this new PSCNN system is compared to the performance of backpropagation models.