Journal
ANNALS OF OPERATIONS RESEARCH
Volume 258, Issue 1, Pages 159-185Publisher
SPRINGER
DOI: 10.1007/s10479-016-2192-6
Keywords
Anti-periodicity; Coincide degree theory; Distributed delay; Global exponential stability; Recurrent neural networks; State-dependent impulsive systems
Categories
Funding
- Middle East Technical University (METU) BAP1 Faculty/Institute Project [BAP-01-01-2015-005]
- TUBITAK (The Scientific and Technological Research Council of Turkey)
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available