State-dependent impulsive Cohen-Grossberg neural networks with time-varying delays

Sayli M., YILMAZ E.

NEUROCOMPUTING, vol.171, pp.1375-1386, 2016 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 171
  • Publication Date: 2016
  • Doi Number: 10.1016/j.neucom.2015.07.095
  • Journal Name: NEUROCOMPUTING
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.1375-1386
  • Keywords: Cohen-Grossberg neural networks, Global exponential stability, State-dependent impulsive systems, Periodicity, GLOBAL EXPONENTIAL STABILITY, DIFFERENTIAL-EQUATIONS, PERIODIC-SOLUTIONS, DYNAMICAL ANALYSIS, EXISTENCE, DISCRETE
  • Middle East Technical University Affiliated: Yes

Abstract

In this paper, a more general class of state-dependent impulsive Cohen-Grossberg neural networks having variable coefficients with time-varying delays is addressed. By means of B-equivalence method, we reduce this state-dependent impulsive neural networks system to a fix time impulsive neural networks system. Sufficient conditions for existence and global exponential stability of the equilibrium point as well as periodic solution are obtained by employing a suitable Lyapunov function, the Banach fixed point theorem and the Halanay-type impulsive differential inequality technique. Finally, two examples with numerical simulations to show the effectiveness of our theoretical results are illustrated. (C) 2015 Elsevier B.V. All rights reserved.