Journal
NEURAL PROCESSING LETTERS
Volume 50, Issue 1, Pages 413-430Publisher
SPRINGER
DOI: 10.1007/s11063-018-9912-7
Keywords
Markov switched stochastic Hopfield neural networks; Discrete time noises; Stability; Lyapunov functionals
Categories
Funding
- National Natural Science Foundation of China [11571024, 61833005, 61773152, 61573096, 61272530]
- China Postdoctoral Science Foundation [2017M621588]
- Science and Technology Research Foundation of Higher Education Institutions of Hebei Province of China [QN2017116]
- Graduate Foundation of North China University of Science and Technology [K1603]
Ask authors/readers for more resources
Recently, a new class of stochastic systems induced by linear discrete time noises was proposed and studied. Up to now, the existing literatures mainly investigated the exponential stability of such stochastic systems under the global Lipschitz condition. Our aim here is to weaken the strictly global Lipschitz condition and explore new stability theory for a new class of Markov switched stochastic Hopfield neural networks induced by nonlinear discrete time noises. In the present paper, we propose such Markov switched stochastic Hopfield neural networks, and creatively introduce a new class of Lyapunov functionals to investigate the H8 stability, asymptotic stability and exponential stability for such systems under the local Lipschitz condition using some novel skills. Furthermore, we specially study the case induced by linear discrete time noises.
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