A simple and high performance neural network for quadratic programming problems
Applied Mathematics and Computation
On Convergence Conditions of an Extended Projection Neural Network
Neural Computation
An Extended Projection Neural Network for Constrained Optimization
Neural Computation
A novel neural network for a class of convex quadratic minimax problems
Neural Computation
A Recurrent Neural Network for Solving a Class of General Variational Inequalities
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A new neural network for solving linear programming problems and its application
IEEE Transactions on Neural Networks
A high-performance neural network for solving linear and quadratic programming problems
IEEE Transactions on Neural Networks
A new neural network for solving linear and quadratic programming problems
IEEE Transactions on Neural Networks
Another K-winners-take-all analog neural network
IEEE Transactions on Neural Networks
Performance analysis for a K-winners-take-all analog neural network: basic theory
IEEE Transactions on Neural Networks
A novel neural network for nonlinear convex programming
IEEE Transactions on Neural Networks
A delayed neural network for solving linear projection equations and its analysis
IEEE Transactions on Neural Networks
A novel neural network for variational inequalities with linear and nonlinear constraints
IEEE Transactions on Neural Networks
Analog neural network for support vector machine learning
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A Simplified Dual Neural Network for Quadratic Programming With Its KWTA Application
IEEE Transactions on Neural Networks
Solving Quadratic Programming Problems by Delayed Projection Neural Network
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Neural network for solving linear programming problems with bounded variables
IEEE Transactions on Neural Networks
K-winners-take-all circuit with O(N) complexity
IEEE Transactions on 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 Systems, Man, and Cybernetics, Part B: Cybernetics
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
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 model of analogue K-winners-take-all neural circuit
Neural Networks
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In this paper, a new recurrent neural network is proposed for solving convex quadratic programming (QP) problems. Compared with existing neural networks, the proposed one features global convergence property under weak conditions, low structural complexity, and no calculation of matrix inverse. It serves as a competitive alternative in the neural network family for solving linear or quadratic programming problems. In addition, it is found that by some variable substitution, the proposed network turns out to be an existing model for solving minimax problems. In this sense, it can be also viewed as a special case of the minimax neural network. Based on this scheme, a k-winners-take-all (k-WTA) network with O(n) complexity is designed, which is characterized by simple structure, global convergence, and capability to deal with some ill cases. Numerical simulations are provided to validate the theoretical results obtained. More importantly, the network design method proposed in this paper has great potential to inspire other competitive inventions along the same line.