Journal of Applied Nonlinear Dynamics
A Neural Network for Solving Nonlinear Convex Programming with Linear Equality and Bounded Constraints
Journal of Applied Nonlinear Dynamics 4(1) (2015) 4352  DOI:10.5890/JAND.2015.03.004
Sitian Qin; Yiming Liu; Changfeng Shao
$^{1}$ School of Control Science and Engineering, Dalian University of Technology, Dalian, China
$^{2}$ Department of Mathematics, Harbin Institute of Technology at Weihai, Weihai, China
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Abstract
In this paper, to solve the nonlinear convex programming problems with linear equality and bounded constraints, a new neural network model is constructed. It is proved that if the initial point lies in the linear equality region, the state of the proposed neural network is convergent to an exact optimal solution of the optimization problem. Compared with the existed neural networks, the proposed in this paper has a low model complexity and avoid estimating the penalty parameters in advance. In the end, several numerical simulations illustrate the effectiveness of the proposed neural network
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