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

Date

2017-11-01

Author

Sayli, Mustafa
YILMAZ, ENES

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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.

Subject Keywords

Management Science and Operations Research, General Decision Sciences

URI

https://hdl.handle.net/11511/64720

Journal

ANNALS OF OPERATIONS RESEARCH

DOI

https://doi.org/10.1007/s10479-016-2192-6

Collections

Department of Mathematics, Article

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Citation Formats

M. Sayli and E. YILMAZ, “Anti-periodic solutions for state-dependent impulsive recurrent neural networks with time-varying and continuously distributed delays,” ANNALS OF OPERATIONS RESEARCH, pp. 159–185, 2017, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/64720.