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Title: On some stability problems of impulsive stochastic Cohen-Grossberg neural networks with mixed time delays
Authors: Tojtovska, Biljana 
Jankovic, Svetlana
Keywords: Lyapunov function Impulsive stochastic neural networks Moment stability Almost sure stability General decay function
Issue Date: 2014
Publisher: Elsevier
Source: B. Tojtovska, S. Jankovic, On some stability problems of impulsive stochastic Cohen-Grossberg neural networks with mixed time delays, Applied Mathematics and Computation, Elsevier, Vol. 239, (2014) 211-226
Project: Partially supported by the Faculty of Computer Science and Engineering at the University ‘‘Ss. Cyril and Methodius’’ in Skopje, as a part of the project ‘‘Modeling and analysis of stochastic neural networks’’. Supported by Grant No. 174007 of MNTRS.
Journal: Applied Mathematics and Computation
Abstract: This paper covers the topic of both the pth moment (p>=2) and almost sure stability of impulsive stochastic Cohen–Grossberg neural networks with mixed time delays. We partially use a known result on exponential stability of impulsive stochastic functional differential systems, based on the Razumikhin type technique, and extend it to the case of stochastic neural networks using the Lyapunov function method and a Gronwall type inequality. Additionally, we consider the stability with respect to a general decay function which includes exponential, but also more general lower rate decay functions as the polynomial and the logarithmic ones. This fact gives us the opportunity to study general decay almost sure stability, even when the exponential one cannot be discussed. Suitable examples which support the theory are also presented.
Appears in Collections:Faculty of Computer Science and Engineering: Journal Articles

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