Convergence analysis of a discrete-time recurrent neural network to perform quadratic real optimization with bound constraints

Presents a model of a discrete-time recurrent neural network designed to perform quadratic real optimization with bound constraints. The network iteratively improves the estimate of the solution, always maintaining it inside of the feasible region. Several neuron updating rules which assure global convergence of the net to the desired minimum have been obtained. Some of them also assure exponential convergence and maximize a lower bound for the convergence degree. Simulation results are presented to show the net performance.