Global robust asymptotic stability of variable-time impulsive BAM neural networks


Sayli M., Yilmaz E.

NEURAL NETWORKS, vol.60, pp.67-73, 2014 (Journal Indexed in SCI) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 60
  • Publication Date: 2014
  • Doi Number: 10.1016/j.neunet.2014.07.016
  • Title of Journal : NEURAL NETWORKS
  • Page Numbers: pp.67-73

Abstract

In this paper, the global robust asymptotic stability of the equilibrium point for a more general class of bidirectional associative memory (BAM) neural networks with variable time of impulses is addressed. Unlike most existing studies, the case of non-fix time impulses is focused on in the present study. By means of B-equivalence method, which was introduced in Akhmet (2003, 2005, 2009, 2010), Akhmet and Perestyuk (1990) and Akhmet and Turan (2009), we reduce these networks to a Fix time impulsive neural networks system. Sufficient conditions ensuring the existence, uniqueness and global robust asymptotic stability of the equilibrium point are obtained by employing an appropriate Lyapunov function and linear matrix inequality (LMI). Finally, we give one illustrative example to show the effectiveness of the theoretical results. (C) 2014 Elsevier Ltd. All rights reserved.