Novel Continuous-and Discrete-Time Neural Networks for Solving Quadratic Minimax Problems With Linear Equality Constraints

Xingbao Gao, Li Zhi Liao

Research output: Contribution to journalJournal articlepeer-review

2 Citations (Scopus)

Abstract

This article presents two novel continuous-and discrete-time neural networks (NNs) for solving quadratic minimax problems with linear equality constraints. These two NNs are established based on the conditions of the saddle point of the underlying function. For the two NNs, a proper Lyapunov function is constructed so that they are stable in the sense of Lyapunov, and will converge to some saddle point(s) for any starting point under some mild conditions. Compared with the existing NNs for solving quadratic minimax problems, the proposed NNs require weaker stability conditions. The validity and transient behavior of the proposed models are illustrated by some simulation results.

Original languageEnglish
Pages (from-to)1-15
Number of pages15
JournalIEEE Transactions on Neural Networks and Learning Systems
DOIs
Publication statusE-pub ahead of print - 27 Jan 2023

Scopus Subject Areas

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

User-Defined Keywords

  • Artificial neural networks
  • Asymptotic stability
  • Computational modeling
  • Convergence
  • Mathematical models
  • neural network (NN)
  • Numerical stability
  • Optimization
  • quadratic minimax problem
  • stability
  • Stability analysis

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