Presents a neural network based near-optimal routing algorithm. It employs a modified Hopfield neural network (MHNN) as a means to solve the shortest path problem. It also guarantees a speedy computation that is appropriate to multi-hop radio networks. The MHNN uses every piece of information that is available at the peripheral neurons in addition to the highly correlated information that is available at the local neuron. Consequently, every neuron converges speedily and optimally to a stable state. The convergence is faster than what is usually found in algorithms that employ conventional Hopfield neural networks. Computer simulations support the indicated claims. The results are relatively independent of network topology for almost all source-destination pairs.
Neural Information Processing, 2002. ICONIP '02. Proceedings of the 9th International Conference on; 12/2002