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

作者：Sayli Mustafa; Yilmaz Enes^*

来源：Annals of Operations Research, 2017, 258(1): 159-185.

DOI：10.1007/s10479-016-2192-6

• Anti-periodicity
• Coincide degree theory
• Distributed delay
• Global exponential stability
• Recurrent neural networks
• State-dependent impulsive systems

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.

• 出版日期2017-11