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Journal of Applied Nonlinear Dynamics
Miguel A. F. Sanjuan (editor), Albert C.J. Luo (editor)
Miguel A. F. Sanjuan (editor)

Department of Physics, Universidad Rey Juan Carlos, 28933 Mostoles, Madrid, Spain


Albert C.J. Luo (editor)

Department of Mechanical and Industrial Engineering, Southern Illinois University Ed-wardsville, IL 62026-1805, USA

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A Neural Network for Solving Nonlinear Convex Programming with Linear Equality and Bounded Constraints

Journal of Applied Nonlinear Dynamics 4(1) (2015) 43--52 | 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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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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