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A Recurrent Neural Network for Solving Nonlinear Convex Programs Subject to Linear Constraints
2005
IEEE Transactions on Neural Networks
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
doi:10.1109/tnn.2004.841779
pmid:17385632
fatcat:dcvgi32m7bcz3ows237zpdgoz4