Computers & Mathematics with Applications
A 2D approach to tomographic image reconstruction using a Hopfield-type neural network
Artificial Intelligence in Medicine
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
A Revised Neural Network for Solving Quadratic Programming Problems
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
Subgradient-based neural networks for nonsmooth nonconvex optimization problems
IEEE Transactions on Neural Networks
Autonomous Autorotation of Unmanned Rotorcraft using Nonlinear Model Predictive Control
Journal of Intelligent and Robotic Systems
Information Sciences: an International Journal
On a Stabilization Problem of Nonlinear Programming Neural Networks
Neural Processing Letters
Computers & Mathematics with Applications
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
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
A generalized global convergence theory of projection-type neural networks for optimization
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Neural networks for solving second-order cone constrained variational inequality problem
Computational Optimization and Applications
BAM-type Cohen-Grossberg neural networks with time delays
Mathematical and Computer Modelling: An International Journal
Solving general convex nonlinear optimization problems by an efficient neurodynamic model
Engineering Applications of Artificial Intelligence
Information Sciences: an International Journal
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In this paper, we propose a recurrent neural network for solving nonlinear convex programming problems with linear constraints. The proposed neural network has a simpler structure and a lower complexity for implementation than the existing neural networks for solving such problems. It is shown here that the proposed neural network is stable in the sense of Lyapunov and globally convergent to an optimal solution within a finite time under the condition that the objective function is strictly convex. Compared with the existing convergence results, the present results do not require Lipschitz continuity condition on the objective function. Finally, examples are provided to show the applicability of the proposed neural network.