Stability Analysis of Gradient-Based Neural Networks for Optimization Problems
Journal of Global Optimization
A Novel Method to Handle Inequality Constraints for Convex Programming Neural Network
Neural Processing Letters
Journal of Global Optimization
Computers & Mathematics with Applications
A Modified Hopfield Network for Nonlinear Programming Problem Solving
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
IEEE Transactions on Neural Networks
Convergence in networks with counterclockwise neural dynamics
IEEE Transactions on Neural Networks
Stability and convergence analysis of a neural model applied in nonlinear systems optimization
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
A neural network for the linear complementarity problem
Mathematical and Computer Modelling: An International Journal
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Two classes of high-performance neural networks for solving linear and quadratic programming problems are given. We prove that the new system converges globally to the solutions of the linear and quadratic programming problems. In a neural network, network parameters are usually not specified. The proposed models can overcome numerical difficulty caused by neural networks with network parameters and obtain desired approximate solutions of the linear and quadratic programming problems