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


Sayli M., Yilmaz E.

ANNALS OF OPERATIONS RESEARCH, cilt.258, sa.1, ss.159-185, 2017 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 258 Sayı: 1
  • Basım Tarihi: 2017
  • Doi Numarası: 10.1007/s10479-016-2192-6
  • Dergi Adı: ANNALS OF OPERATIONS RESEARCH
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.159-185
  • Anahtar Kelimeler: Anti-periodicity, Coincide degree theory, Distributed delay, Global exponential stability, Recurrent neural networks, State-dependent impulsive systems, GLOBAL EXPONENTIAL STABILITY, FUNCTIONAL-DIFFERENTIAL EQUATIONS, ALMOST-PERIODIC SOLUTIONS, SHUNTING INHIBITORY CNNS, EXISTENCE, PERTURBATIONS, SYSTEMS
  • Gazi Üniversitesi Adresli: Evet

Özet

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.