Neural network for finding optimal path in packet-switched network

Neural networks are very good candidates for solving different ill-defined problems, due to their high computational speed and the possibility of working with uncertain data. Among others, they represent an efficient tool for solving constrained optimization problems. Under appropriate assumptions, routing in packet-switched networks may be considered as an optimization problem, more precisely, as a shortest-path problem, where the Hopfield type neural network exhibits very good performance. An efficient neural network shortest-path algorithm, inspired by the Hopfield network, is suggested. The routing algorithm suggested is designed to find the shortest path but also it takes into account packet-loss avoidance. The applicability of the proposed model is demonstrated through computer simulations for different full-connected networks with both symmetrical and non-symmetrical links.