A neural network for a class of convex quadratic minimax problems with constraints

Xing Bao Gao, Lizhi LIAO*, Weimin Xue

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

37 Citations (Scopus)

Abstract

In this paper, we propose a neural network for solving a class of convex quadratic minimax problems with constraints. Four sufficient conditions are provided to ensure the asymptotic stability of the proposed network. Furthermore, the exponential stability of the proposing network is also proved under certain conditions. The results obtained here can be further extended to the globally projected dynamical system. In addition, some new stability conditions for the system are also obtained. Since our stability conditions can be easily checked in practice, these results becomes more attractive in real applications.

Original languageEnglish
Pages (from-to)622-628
Number of pages7
JournalIEEE Transactions on Neural Networks
Volume15
Issue number3
DOIs
Publication statusPublished - May 2004

Scopus Subject Areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

User-Defined Keywords

  • Convergence and stability
  • Minimax problem
  • Neural network
  • Saddle point

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