A smoothed NR neural network for solving nonlinear convex programs with second-order cone constraints

Xinhe Miao, Jein-Shan Chen, Chun Hsu Ko

研究成果: 雜誌貢獻期刊論文同行評審

12 引文 斯高帕斯(Scopus)

摘要

This paper proposes a neural network approach for efficiently solving general nonlinear convex programs with second-order cone constraints. The proposed neural network model was developed based on a smoothed natural residual merit function involving an unconstrained minimization reformulation of the complementarity problem. We study the existence and convergence of the trajectory of the neural network. Moreover, we show some stability properties for the considered neural network, such as the Lyapunov stability, asymptotic stability, and exponential stability. The examples in this paper provide a further demonstration of the effectiveness of the proposed neural network. This paper can be viewed as a follow-up version of [20,26] because more stability results are obtained.

原文英語
頁(從 - 到)255-270
頁數16
期刊Information Sciences
268
DOIs
出版狀態已發佈 - 2014 六月 1

ASJC Scopus subject areas

  • 軟體
  • 控制與系統工程
  • 理論電腦科學
  • 電腦科學應用
  • 資訊系統與管理
  • 人工智慧

指紋

深入研究「A smoothed NR neural network for solving nonlinear convex programs with second-order cone constraints」主題。共同形成了獨特的指紋。

引用此