Anti-periodic solutions for state-dependent impulsive recurrent neural networks with time-varying and continuously distributed delays


Sayli M., YILMAZ E.

ANNALS OF OPERATIONS RESEARCH, vol.258, no.1, pp.159-185, 2017 (Peer-Reviewed Journal) identifier identifier

  • Publication Type: Article / Article
  • Volume: 258 Issue: 1
  • Publication Date: 2017
  • Doi Number: 10.1007/s10479-016-2192-6
  • Journal Name: ANNALS OF OPERATIONS RESEARCH
  • Journal Indexes: Science Citation Index Expanded, Scopus
  • Page Numbers: pp.159-185
  • Keywords: Anti-periodicity, Coincide degree theory, Distributed delay, Global exponential stability, Recurrent neural networks, State-dependent impulsive systems, GLOBAL EXPONENTIAL STABILITY, FUNCTIONAL-DIFFERENTIAL EQUATIONS, ALMOST-PERIODIC SOLUTIONS, SHUNTING INHIBITORY CNNS, EXISTENCE, PERTURBATIONS, SYSTEMS

Abstract

In this paper, we address a new model of neural networks related to the impulsive phenomena which is called state-dependent impulsive recurrent neural networks with time-varying and continuously distributed delays. We investigate sufficient conditions on the existence and uniqueness of exponentially stable anti-periodic solution for these neural networks by employing method of coincide degree theory and an appropriate Lyapunov function. Moreover, we present an illustrative example to show the effectiveness and feasibility of the obtained theoretical results.