Neural networks for shortest path computation and routing in computer networks.

The application of neural networks to the optimum routing problem in packet-switched computer networks, where the goal is to minimize the network-wide average time delay, is addressed. Under appropriate assumptions, the optimum routing algorithm relies heavily on shortest path computations that have to be carried out in real time. For this purpose an efficient neural network shortest path algorithm that is an improved version of previously suggested Hopfield models is proposed. The general principles involved in the design of the proposed neural network are discussed in detail. Its computational power is demonstrated through computer simulations. One of the main features of the proposed model is that it will enable the routing algorithm to be implemented in real time and also to be adaptive to changes in link costs and network topology.