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

*此作品的通信作者

研究成果: 雜誌貢獻期刊論文同行評審

6 引文 斯高帕斯(Scopus)

摘要

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 Fischer-Burmeister 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.

原文英語
頁(從 - 到)62-72
頁數11
期刊Neurocomputing
203
DOIs
出版狀態已發佈 - 2016 八月 26

ASJC Scopus subject areas

  • 電腦科學應用
  • 認知神經科學
  • 人工智慧

指紋

深入研究「A neural network based on the generalized FB function for nonlinear convex programs with second-order cone constraints」主題。共同形成了獨特的指紋。

引用此