New stability results for Takagi-Sugeno fuzzy Cohen-Grossberg neural networks with multiple delays

Sevgen S.

NEURAL NETWORKS, cilt.114, ss.60-66, 2019 (SCI-Expanded, Scopus) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 114
  • Basım Tarihi: 2019
  • Doi Numarası: 10.1016/j.neunet.2019.02.010
  • Dergi Adı: NEURAL NETWORKS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.60-66
  • Anahtar Kelimeler: Fuzzy systems, Cohen-Grossberg neural networks, Time delays, Lyapunov stability theorems, ROBUST EXPONENTIAL STABILITY, ACTIVATION FUNCTIONS, TIME-DELAYS, MULTISTABILITY, DISSIPATIVITY, DESIGN, SYSTEM
  • İstanbul Üniversitesi-Cerrahpaşa Adresli: Evet

Özet

This work focuses on global asymptotic stability of Takagi-Sugeno fuzzy Cohen-Grossberg neural networks with multiple time delays. By using the standard Lyapunov stability techniques and non-singular M-matrix condition of matrices together with employing the nonlinear Lipschitz activation functions, a new easily verifiable sufficient criterion is obtained to guarantee global asymptotic stability of the Cohen-Grossberg neural network model which is represented by a Takagi-Sugeno fuzzy model. A constructive numerical example is studied to demonstrate the effectiveness of the proposed theoretical results. This numerical example is also used to make a comparison between the global stability condition obtained in this study and some of previously published global stability results. This comparison reveals that the condition we propose establishes a novel and alternative stability result for Takagi-Sugeno fuzzy Cohen-Grossberg neural networks of this class. (c) 2019 Elsevier Ltd. All rights reserved.