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


Sayli M., Yilmaz E.

NEURAL NETWORKS, vol.60, pp.67-73, 2014 (Peer-Reviewed Journal) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 60
  • Publication Date: 2014
  • Doi Number: 10.1016/j.neunet.2014.07.016
  • Journal Name: NEURAL NETWORKS
  • Journal Indexes: Science Citation Index Expanded, Scopus
  • Page Numbers: pp.67-73
  • Keywords: Global robust asymptotic stability, Impulsive BAM neural networks, Asymptotic stability, Linear matrix inequality, BIDIRECTIONAL ASSOCIATIVE MEMORIES, ALMOST-PERIODIC SOLUTIONS, EXPONENTIAL STABILITY, VARYING DELAYS, DISTRIBUTED DELAYS, DIFFERENTIAL-EQUATIONS, HOPF-BIFURCATION, EXISTENCE, PRESERVATION, SYSTEM

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.