An improved Lyapunov functional with application to stability of Cohen-Grossberg neural networks of neutral-type with multiple delays

FAYDASIÇOK Ö.

NEURAL NETWORKS, cilt.132, ss.532-539, 2020 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 132
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1016/j.neunet.2020.09.023
  • Dergi Adı: NEURAL NETWORKS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Applied Science & Technology Source, BIOSIS, Biotechnology Research Abstracts, Communication Abstracts, Compendex, Computer & Applied Sciences, EMBASE, INSPEC, MEDLINE, Psycinfo, zbMATH
  • Sayfa Sayıları: ss.532-539
  • Anahtar Kelimeler: Delayed neural networks, Neutral systems, Global stability analysis, Lyapunov functionals, GLOBAL EXPONENTIAL STABILITY, DEPENDENT STABILITY, FEEDBACK CONTROL, TIME, SYSTEMS, CRITERIA, STORAGE
  • İstanbul Üniversitesi Adresli: Evet

Özet

The essential objective of this research article is to investigate stability issue of neutral-type Cohen- Grossberg neural networks involving multiple time delays in states of neurons and multiple neutral delays in time derivatives of states of neurons in the network. By exploiting a modified and improved version of a previously introduced Lyapunov functional, a new sufficient stability criterion is obtained for global asymptotic stability of Cohen-Grossberg neural networks of neutral-type possessing multiple delays. The proposed new stability condition does not involve the time and neutral delay parameters. The obtained stability criterion is totally dependent on the system elements of Cohen-Grossberg neural network model. Moreover, the validity of this novel global asymptotic stability condition may be tested by only checking simple appropriate algebraic equations established within the parameters of the considered neutral-type neural network. In addition, an instructive numerical example is presented to indicate the advantages of our proposed stability result over the existing literature results obtained for stability of various classes of neutral-type neural networks having multiple delays. (c) 2020 Elsevier Ltd. All rights reserved.