A discrete-time recurrent neural network for shortest-path routing

Presents a discrete-time recurrent neural network, with a fixed step parameter, for solving the shortest path problem. The proposed discrete-time recurrent neural network with a simple architecture is proven to be globally convergent to exact optimal solutions and is suitable for hardware implementation. Furthermore, an improved network with a larger step size independent of the problem size is proposed to increase its convergence rate. The performance and operating characteristics of the proposed neural network are demonstrated by means of simulation results.