title: Chaotifying delayed recurrent neural networks via impulsive effects.
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.
authors.affiliation: Department of Mathematics, Middle East Technical University, 06800 Ankara, Turkey.
authors.forename: Mustafa
authors.initials: M
authors.lastname: Şaylı
authors.affiliation: Department of Mathematics, Polatli Faculty of Science and Arts, Gazi University, 06900 Ankara, Turkey.
authors.forename: Enes
authors.initials: E
authors.lastname: Yılmaz