A neural network based on the generalized Fischer-Burmeister function for nonlinear complementarity problems

Jein-Shan Chen, Chun Hsu Ko, Shaohua Pan

研究成果: 雜誌貢獻文章

31 引文 斯高帕斯(Scopus)

摘要

In this paper, we consider a neural network model for solving the nonlinear complementarity problem (NCP). The neural network is derived from an equivalent unconstrained minimization reformulation of the NCP, which is based on the generalized Fischer-Burmeister function φ{symbol}p (a, b) = {norm of matrix} (a, b) {norm of matrix}p - (a + b). We establish the existence and the convergence of the trajectory of the neural network, and study its Lyapunov stability, asymptotic stability as well as exponential stability. It was found that a larger p leads to a better convergence rate of the trajectory. Numerical simulations verify the obtained theoretical results.

原文英語
頁(從 - 到)697-711
頁數15
期刊Information Sciences
180
發行號5
DOIs
出版狀態已發佈 - 2010 三月 1

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications
  • Information Systems and Management

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