A Novel Hybrid Training Method for Hopfield Neural Networks Applied to Routing in Communications Networks
Willi Schüler, Carmelo J. A. Bastos-Filho, Adriano L. I. Oliveira · 2007
Efficient routing algorithms are very important for the operation of communication networks, including the Internet. This article proposes a novel hybrid intelligent method for routing which combines Hopfield neural networks (HNN) and simulated annealing (SA). The proposed method introduces a modified version of the discrete-time equation used by Bastos-Filho et al [1]. The novel version of the equation aims to improve the HNN convergence, thereby decreasing the computation cost. In our method, the SA algorithm is used to obtain the optimal parameters of the HNN. Simulations reported in this paper shows that the proposed method outperforms the method of Bastos-Filho et al [1], by computing routes using smaller number of iterations and smaller error.