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

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  • Mustafa Şaylı

    (Middle East Technical University
    University of Nottingham)

  • Enes Yılmaz

    (Gazi University
    Princeton University)

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.

Suggested Citation

  • Mustafa Şaylı & Enes Yılmaz, 2017. "Anti-periodic solutions for state-dependent impulsive recurrent neural networks with time-varying and continuously distributed delays," Annals of Operations Research, Springer, vol. 258(1), pages 159-185, November.
    • Handle: RePEc:spr:annopr:v:258:y:2017:i:1:d:10.1007_s10479-016-2192-6
    DOI: 10.1007/s10479-016-2192-6
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    References listed on IDEAS

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    1. Li, Yongkun & Xing, Wenya & Lu, Linghong, 2006. "Existence and global exponential stability of periodic solution of a class of neural networks with impulses," Chaos, Solitons & Fractals, Elsevier, vol. 27(2), pages 437-445.
    2. Li, Yongkun & Xing, Zhiwei, 2007. "Existence and global exponential stability of periodic solution of CNNs with impulses," Chaos, Solitons & Fractals, Elsevier, vol. 33(5), pages 1686-1693.
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    Cited by:

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