An efficient model of neural networks for dynamic programming

Systems based on artificial neural networks have high computational rates owing to the use of a massive number of simple processing elements and the high degree of connectivity between these elements. Neural networks with feedback connections provide a computing model capable of solving a large class of optimization problems. This paper presents a novel approach for solving dynamic programming problems using artificial neural networks. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the convergence of the network to the equilibrium points. Simulated examples are presented and compared with other neural networks. The results demonstrate that the proposed method gives a significant improvement.