Chaotifying delayed recurrent neural networks via impulsive effects


Sayil M., YILMAZ E.

CHAOS, cilt.26, 2016 (SCI İndekslerine Giren Dergi) identifier identifier

  • Cilt numarası: 26 Konu: 2
  • Basım Tarihi: 2016
  • Doi Numarası: 10.1063/1.4941852
  • Dergi Adı: CHAOS

Özet

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.