New robust stability results for bidirectional associative memory neural networks with multiple time delays

Senan S., Arik S., LIU D.

APPLIED MATHEMATICS AND COMPUTATION, cilt.218, sa.23, ss.11472-11482, 2012 (SCI-Expanded, Scopus) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 218 Sayı: 23
  • Basım Tarihi: 2012
  • Doi Numarası: 10.1016/j.amc.2012.04.075
  • Dergi Adı: APPLIED MATHEMATICS AND COMPUTATION
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.11472-11482
  • Anahtar Kelimeler: Equilibrium and stability analysis, Bidirectional associative memory neural networks, Lyapunov functionals, GLOBAL ASYMPTOTIC STABILITY, EXPONENTIAL STABILITY, DISTRIBUTED DELAYS, VARYING DELAYS, CRITERION, DISCRETE, PASSIVITY
  • İstanbul Üniversitesi-Cerrahpaşa Adresli: Evet

Özet

In this paper, the robust stability problem is investigated for a class of bidirectional associative memory (BAM) neural networks with multiple time delays. By employing suitable Lyapunov functionals and using the upper bound norm for the interconnection matrices of the neural network system, some novel sufficient conditions ensuring the existence, uniqueness and global robust stability of the equilibrium point are derived. The obtained results impose constraint conditions on the system parameters of neural network independent of the delay parameters. Some numerical examples and simulation results are given to demonstrate the applicability and effectiveness of our results, and to compare the results with previous robust stability results derived in the literature. (C) 2012 Elsevier Inc. All rights reserved.