Modeling of feedforward neural network in PAHRA architecture
Liberios Vokorokos, Norbert Ádám · 2009
One of the most popular neural networks are multilayered feedforward neural networks, which represent the most standard configuration of biological inspired mathematical models of simplified neural system. These networks represent massive parallel systems with a high number of simple process elements and therefore it is natural to try to implement this kind of systems on parallel computer architecture. The parallel architecture described in this article provides flexible platform for simulation of multilayered feedforward neural networks trained with back-propagation algorithm. The computation model of given architecture allows formally describe components of parallel implementation of neural network and provides mathematical tool for verification of system performance.