An improved stability result for delayed Takagi-Sugeno fuzzy Cohen-Grossberg neural networks

Orman Z.

NEURAL NETWORKS, cilt.108, ss.445-451, 2018 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 108
  • Basım Tarihi: 2018
  • Doi Numarası: 10.1016/j.neunet.2018.09.008
  • Dergi Adı: NEURAL NETWORKS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.445-451
  • İstanbul Üniversitesi Adresli: Hayır

Özet

This work proposes a novel and improved delay independent global asymptotic stability criterion for delayed Takagi-Sugeno (T-S) fuzzy Cohen-Grossberg neural networks exploiting a suitable fuzzy-type Lyapunov functional in the presence of the nondecreasing activation functions having bounded slopes. The proposed stability criterion can be easily validated as it is completely expressed in terms of the system matrices of the fuzzy neural network model considered. It will be shown that the stability criterion obtained in this work for this type of fuzzy neural networks improves and generalizes some of the previously published stability results. A constructive numerical example is also given to support the proposed theoretical results. (c) 2018 Elsevier Ltd. All rights reserved.