A neural network based on the generalized FB function for nonlinear convex programs with second-order cone constraints

Xinhe Miao Jein-Shan Chen Chun-Hsu Ko

Numerical Analysis and Scientific Computing mathscidoc:1910.43922

Neurocomputing, 203, 62-72, 2016.8
This paper proposes a neural network approach to efficiently solve nonlinear convex programs with the second-order cone constraints. The neural network model is designed by the generalized FischerBurmeister function associated with second-order cone. We study the existence and convergence of the trajectory for the considered neural network. Moreover, we also show stability properties for the considered neural network, including the Lyapunov stability, the asymptotic stability and the exponential stability. Illustrative examples give a further demonstration for the effectiveness of the proposed neural network. Numerical performance based on the parameter being perturbed and numerical comparison with other neural network models are also provided. In overall, our model performs better than two comparative methods.
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@inproceedings{xinhe2016a,
  title={A neural network based on the generalized FB function for nonlinear convex programs with second-order cone constraints},
  author={Xinhe Miao, Jein-Shan Chen, and Chun-Hsu Ko},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191020224903242567451},
  booktitle={Neurocomputing},
  volume={203},
  pages={62-72},
  year={2016},
}
Xinhe Miao, Jein-Shan Chen, and Chun-Hsu Ko. A neural network based on the generalized FB function for nonlinear convex programs with second-order cone constraints. 2016. Vol. 203. In Neurocomputing. pp.62-72. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191020224903242567451.
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