Robust Model Predictive Control Using a Discrete-Time Recurrent Neural Network
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
Permitted and forbidden sets in discrete-time linear threshold recurrent neural networks
IEEE Transactions on Neural Networks
A discrete-time neural network for optimization problems with hybrid constraints
IEEE Transactions on Neural Networks
Discrete-time ZD, GD and NI for solving nonlinear time-varying equations
Numerical Algorithms
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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