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
A Gradient-based Continuous Method for Large-scale Optimization Problems
Journal of Global Optimization
A novel neural network for a class of convex quadratic minimax problems
Neural Computation
A new neural network for solving nonlinear projection equations
Neural Networks
A neural network approach for solving nonlinear bilevel programming problem
Computers & Mathematics with Applications
Neurodynamic Analysis for the Schur Decomposition of the Box Problems
Computational Intelligence and Security
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
A Neural Network Model for Solving Nonlinear Optimization Problems with Real-Time Applications
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
A new projection-based neural network for constrained variational inequalities
IEEE Transactions on Neural Networks
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
On a Stabilization Problem of Nonlinear Programming Neural Networks
Neural Processing Letters
A neural network for solving a convex quadratic bilevel programming problem
Journal of Computational and Applied Mathematics
A neural network approach for solving mathematical programs with equilibrium constraints
Expert Systems with Applications: An International Journal
Parallelism in binary hopfield networks
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
A hopfiled neural network for nonlinear constrained optimization problems based on penalty function
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
A neural network with finite-time convergence for a class of variational inequalities
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
A recurrent neural network for linear fractional programming with bound constraints
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
A recurrent neural network for extreme eigenvalue problem
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - 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
ICICA'11 Proceedings of the Second international conference on Information Computing and Applications
A hierarchical optimization neural network for large-scale dynamic systems
Automatica (Journal of IFAC)
| Hi-index | 0.01 |
We present a general methodology for designing optimization neural networks. We prove that the neural networks constructed by using the proposed method are guaranteed to be globally convergent to solutions of problems with bounded or unbounded solution sets, in contrast with the gradient methods whose convergence is not guaranteed. We show that the proposed method contains both the gradient methods and nongradient methods employed in existing optimization neural networks as special cases. Based on the theoretical results of the proposed method, we study the convergence and stability of general gradient models in the case of unisolated solutions. Using the proposed method, we derive some new neural network models for a very large class of optimization problems, in which the equilibrium points correspond to exact solutions and there is no variable parameter. Finally, some numerical examples show the effectiveness of the method