New conditions for global stability of neutral-type delayed Cohen-Grossberg neural networks

Ozcan N.

NEURAL NETWORKS, cilt.106, ss.1-7, 2018 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 106
  • Basım Tarihi: 2018
  • Doi Numarası: 10.1016/j.neunet.2018.06.009
  • Dergi Adı: NEURAL NETWORKS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.1-7
  • Anahtar Kelimeler: Lyapunov stability theorems, Neutral systems, Neural networks, Matrix theory, TIME-VARYING DELAYS, MARKOVIAN JUMP PARAMETERS, EXPONENTIAL STABILITY, DEPENDENT STABILITY, DISTRIBUTED DELAYS, DISCRETE, SIGNALS, SYSTEMS
  • Bursa Uludağ Üniversitesi Adresli: Evet

Özet

This paper carries out a theoretical investigation of the class of neutral-type delayed Cohen-Grossberg neural networks by using the Lyapunov stability theory. By employing a suitable Lyapunov functional candidate, we derive some new delay independent sufficient conditions for the global asymptotic stability of the equilibrium point for the neutral-type Cohen-Grossberg neural networks with time delays. The obtained stability conditions can be completely characterized by the networks parameters of the neutral systems under consideration. Therefore, it is easy to verify the applicability of our results by simply using some algebraic manipulations of the conditions. Some numerical examples are also given to show the effectiveness of the derived analytical results. A detailed comparison between our proposed results and recently reported corresponding stability results is also made, revealing that the conditions given in this paper establish a new set of stability criteria for Neutral-Type Cohen-Grossberg Neural Networks. (C) 2018 Elsevier Ltd. All rights reserved.