Chaotifying delayed recurrent neural networks via impulsive effects

Sayil M., YILMAZ E.

CHAOS, vol.26, no.2, 2016 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 26 Issue: 2
  • Publication Date: 2016
  • Doi Number: 10.1063/1.4941852
  • Journal Name: CHAOS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Middle East Technical University Affiliated: Yes

Abstract

In this paper, chaotification of delayed recurrent neural networks via chaotically changing moments of impulsive actions is considered. Sufficient conditions for the presence of Li-Yorke chaos with its ingredients proximality, frequent separation, and existence of infinitely many periodic solutions are theoretically proved. Finally, effectiveness of our theoretical results is illustrated by an example with numerical simulations. (C) 2016 AIP Publishing LLC.