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 efficient tool for solving constrained, optimization problems. Under appropriate, assumptions, the routing in packet-switched networks may be considered as optimization problem, more precisely as a shortest-path problem, when the Hopfield type neural network exhibits very good performances. The efficient neural network shortest path algorithm inspired by Hopfield network is suggested. The routing algorithm suggested is designed to find the shortest path but also it takes into account the packet-loss avoiding. The applicability of the proposed model is demonstrated through computer simulations for different full-connected networks with both symmetrical and non-symmetrical links.