Stability and convergence analysis for a class of neural networks

Xingbao Gao*, Lizhi LIAO

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

In this paper, we analyze and establish the stability and convergence of the dynamical system proposed by Xia and Feng, whose equilibria solve variational inequality and related problems. Under the pseudo-monotonicity and other conditions, this system is proved to be stable in the sense of Lyapunov and converges to one of its equilibrium points for any starting point. Meanwhile, the global exponential stability of this system is also shown under some mild conditions without the strong monotonicity of the mapping. The obtained results improve and correct some existing ones. The validity and performance of this system are demonstrated by some numerical examples.

Original languageEnglish
Article number6031924
Pages (from-to)1770-1782
Number of pages13
JournalIEEE Transactions on Neural Networks
Volume22
Issue number11
DOIs
Publication statusPublished - Nov 2011

Scopus Subject Areas

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

User-Defined Keywords

  • Convergence
  • exponential stability
  • neural network
  • variational inequality

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