Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
9953307 | Statistics & Probability Letters | 2018 | 16 Pages |
Abstract
This paper studies the mean-square exponential input-to-state stability for a class of delayed impulsive stochastic Cohen-Grossberg neural networks driven by G-Brownian motion. By constructing an appropriate G-Lyapunov-Krasovskii functional, mathematical induction approach and some inequality techniques, a new set of sufficient conditions is obtained for the mean-square exponential input-to-state stability of the trivial solutions for the considered systems. Finally, an example is given to illustrate the obtained theory.
Keywords
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Physical Sciences and Engineering
Mathematics
Statistics and Probability
Authors
Yong Ren, Qian He, Yuanfang Gu, R. Sakthivel,