New criteria for global stability of neutral-type Cohen-Grossberg neural networks with multiple delays

FAYDASIÇOK Ö.

NEURAL NETWORKS, vol.125, pp.330-337, 2020 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 125
  • Publication Date: 2020
  • Doi Number: 10.1016/j.neunet.2020.02.020
  • 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, EMBASE, INSPEC, MEDLINE, Psycinfo, zbMATH
  • Page Numbers: pp.330-337
  • Keywords: Neutral systems, Delayed neural networks, Stability analysis, Lyapunov stability theorems, TIME-VARYING DELAY, EXPONENTIAL STABILITY, DEPENDENT STABILITY, DISTRIBUTED DELAYS, SYSTEMS, DISCRETE, STORAGE, DESIGN
  • Istanbul University Affiliated: Yes

Abstract

The significant contribution of this paper is the addressing the stability issue of neutral-type Cohen-Grossberg neural networks possessing multiple time delays in the states of the neurons and multiple neutral delays in time derivative of states of the neurons. By making the use of a novel and enhanced Lyapunov functional, some new sufficient stability criteria are presented for this model of neutral-type neural systems. The obtained stability conditions are completely dependent of the parameters of the neural system and independent of time delays and neutral delays. A constructive numerical example is presented for the sake of proving the key advantages of the proposed stability results over the previously reported corresponding stability criteria for Cohen-Grossberg neural networks of neutral type. Since, stability analysis of Cohen-Grossberg neural networks involving multiple time delays and multiple neutral delays is a difficult problem to overcome, the investigations of the stability conditions of the neutral-type the stability analysis of this class of neural network models have not been given much attention. Therefore, the stability criteria derived in this work can be evaluated as a valuable contribution to the stability analysis of neutral-type Cohen-Grossberg neural systems involving multiple delays. (c) 2020 Elsevier Ltd. All rights reserved.