Neural network based routing in computer communication networks

A neural-network-based routing algorithm is presented which demonstrates the ability to take into account simultaneously the shortest path and the channel capacity in computer communication networks. A Hopfield-type of neural-network architecture is proposed to provide the necessary connections and weights, and it is considered as a massively parallel distributed processing system with the ability to reconfigure a route through dynamic learning. This provides an optimum transmission path from the source node to the destination node. The traffic conditions measured throughout the system have been investigated. No congestion occurs in this network because it adjusts to the changes in the status of weights and provides a dynamic response according to the input traffic load. Simulation of a ten-node communication network shows not only the efficiency but also the capability of generating a route if broken links occur or the channels are saturated