Chaotifying delayed recurrent neural networks via impulsive effects

Date

2016-02-01

Author

Sayil, Mustafa
YILMAZ, ENES

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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.

Subject Keywords

Mathematical Physics, General Physics and Astronomy, Applied Mathematics, Statistical and Nonlinear Physics

URI

https://hdl.handle.net/11511/64938

Journal

CHAOS

DOI

https://doi.org/10.1063/1.4941852

Collections

Department of Mathematics, Article

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Citation Formats

M. Sayil and E. YILMAZ, “Chaotifying delayed recurrent neural networks via impulsive effects,” CHAOS, pp. 0–0, 2016, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/64938.