A novel neural network for a class of convex quadratic minimax problems
Neural Computation
Computers & Mathematics with Applications
A new neural network for solving nonlinear projection equations
Neural Networks
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
A new projection-based neural network for constrained variational inequalities
IEEE Transactions on Neural Networks
Subgradient-based neural networks for nonsmooth nonconvex optimization problems
IEEE Transactions on Neural Networks
On a Stabilization Problem of Nonlinear Programming Neural Networks
Neural Processing Letters
Design of recurrent neural networks for solving constrained least absolute deviation problems
IEEE Transactions on Neural Networks
A dynamical model for solving degenerate quadratic minimax problems with constraints
Journal of Computational and Applied Mathematics
A neural network for constrained saddle point problems: an approximation approach
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
A novel neural network for solving singular nonlinear convex optimization problems
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
| Hi-index | 0.00 |
In this paper, we propose a neural network for solving a class of convex quadratic minimax problems with constraints. Four sufficient conditions are provided to ensure the asymptotic stability of the proposed network. Furthermore, the exponential stability of the proposing network is also proved under certain conditions. The results obtained here can be further extended to the globally projected dynamical system. In addition, some new stability conditions for the system are also obtained. Since our stability conditions can be easily checked in practice, these results becomes more attractive in real applications.