The combined Lyapunov functionals method for stability analysis of neutral Cohen–Grossberg neural networks with multiple delays

FAYDASIÇOK Ö., ARIK S.

Neural Networks, vol.180, 2024 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 180
  • Publication Date: 2024
  • Doi Number: 10.1016/j.neunet.2024.106641
  • Journal Name: Neural Networks
  • Journal Indexes: 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, INSPEC, MEDLINE, Psycinfo, zbMATH
  • Keywords: Lyapunov stability theorem, Matrix theory, Multiple delays, Neural networks, Neutral systems
  • Istanbul University Affiliated: Yes

Abstract

This research article will employ the combined Lyapunov functionals method to deal with stability analysis of a more general type of Cohen–Grossberg neural networks which simultaneously involve constant time and neutral delay parameters. By utilizing some combinations of various Lyapunov functionals, we determine novel criteria ensuring global stability of such a model of neural systems that employ Lipschitz continuous activation functions. These proposed results are totally stated independently of delay terms and they can be completely characterized by the constants parameters involved in the neural system. By making some detailed analytical comparisons between the stability results derived in this research article and the existing corresponding stability criteria obtained in the past literature, we prove that our proposed stability results lead to establishing some sets of stability conditions and these conditions may be evaluated as different alternative results to the previously reported corresponding stability criteria. A numerical example is also presented to show the applicability of the proposed stability results.