Nonlinear optimization using a modified Hopfield model - da Silva, I. N.; de Arruda, LVR; do Amaral, W. C. (Institute of Electrical and Electronics Engineers (IEEE), 1998)
Systems based on artificial neural networks have high computational rates due to the use of a massive number of simple processing elements. Neural networks with feedback connections provide a computing model capable of solving a rich class of optimization problems. In this paper, a modified Hopfield network is developed for solving constrained nonlinear optimization problems. The internal parameters of the network are obtained using the valid-subspace technique. Simulated examples are presented as an illustration of the proposed approach.