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

Sayli M., Yilmaz E.

NEURAL NETWORKS, cilt.60, ss.67-73, 2014 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 60
  • Basım Tarihi: 2014
  • Doi Numarası: 10.1016/j.neunet.2014.07.016
  • Dergi Adı: NEURAL NETWORKS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.67-73
  • Anahtar Kelimeler: 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
  • Gazi Üniversitesi Adresli: Evet

Özet

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.