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

Mustafa Şaylı () and Enes Yılmaz ()
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Mustafa Şaylı: Middle East Technical University
Enes Yılmaz: Gazi University

Annals of Operations Research, 2017, vol. 258, issue 1, No 8, 159-185

Abstract: Abstract 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.

Keywords: Anti-periodicity; Coincide degree theory; Distributed delay; Global exponential stability; Recurrent neural networks; State-dependent impulsive systems (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s10479-016-2192-6

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