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
Interactive Learning Neural Networks for Predicting Game Behavior
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
Another Simple Recurrent Neural Network for Quadratic and Linear Programming
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
IEEE Transactions on Neural Networks
Real time identification and control of dynamic systems using recurrent neural networks
Artificial Intelligence Review
Solving convex optimization problems using recurrent neural networks in finite time
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
A locally recurrent fuzzy neural network with support vector regression for dynamic-system modeling
IEEE Transactions on Fuzzy Systems
A new one-layer neural network for linear and quadratic programming
IEEE Transactions on Neural Networks
Design of recurrent neural networks for solving constrained least absolute deviation problems
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Solving the assignment problem with the improved dual neural network
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part I
A new neural network for solving nonlinear programming problems
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part I
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
Solving general convex nonlinear optimization problems by an efficient neurodynamic model
Engineering Applications of Artificial Intelligence
Recurrent networks for compressive sampling
Neurocomputing
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In this paper, a one-layer recurrent neural network with a discontinuous hard-limiting activation function is proposed for quadratic programming. This neural network is capable of solving a large class of quadratic programming problems. The state variables of the neural network are proven to be globally stable and the output variables are proven to be convergent to optimal solutions as long as the objective function is strictly convex on a set defined by the equality constraints. In addition, a sequential quadratic programming approach based on the proposed recurrent neural network is developed for general nonlinear programming. Simulation results on numerical examples and support vector machine (SVM) learning show the effectiveness and performance of the neural network.