Stability analysis of Cohen-Grossberg neural networks of neutral-type: Multiple delays case


Ozcan N.

NEURAL NETWORKS, vol.113, pp.20-27, 2019 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 113
  • Publication Date: 2019
  • Doi Number: 10.1016/j.neunet.2019.01.017
  • Journal Name: NEURAL NETWORKS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.20-27
  • Bursa Uludag University Affiliated: Yes

Abstract

The essential purpose of this work is to conduct an investigation into stability problem for the class of neutral-type Cohen-Grossberg neural networks including multiple time delays in states and multiple neutral delays in time derivative of states. By proposing an appropriate Lyapunov functional, a new sufficient criterion is derived for global asymptotic stability of neutral-type neural networks. The obtained stability criterion is independent of time delay and neutral delay parameters, and it is completely stated in terms of the elements of interconnection matrices and other network parameters. Thus, this newly presented stability condition can be validated by simply examining some algebraic equations establishing some relationships among the system parameters and matrices of the neutral-type neural system. A constructive example is presented to indicate applicability of the obtained sufficient stability condition. Since stability analysis of the class of neutral-type neural networks considered in this work has not been given much attention due to the difficulty of developing efficient mathematical techniques and methods enabling to conduct a stability analysis of such neutral-type neural systems, the criterion proposed in this paper can be considered as a leading stability result for neutral-type Cohen-Grossberg neural systems including multiple time and multiple neutral delays. (C) 2019 Elsevier Ltd. All rights reserved.