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Stochastic Dynamics of Nonautonomous Cohen‐Grossberg NeuralNetworks

Stochastic dynamics of nonautonomous Cohen-Grossberg neural networks
Authors: Huang, Chuangxia; Cao, Jinde;

Stochastic Dynamics of Nonautonomous Cohen‐Grossberg NeuralNetworks

Abstract

This paper is devoted to the study of the stochastic stability of a class of Cohen‐Grossberg neural networks, in which the interconnections and delays are time‐varying. With the help of Lyapunov function, Burkholder‐Davids‐Gundy inequality, and Borel‐Cantell′s theory, a set of novel sufficient conditions on pth moment exponential stability and almost sure exponential stability for the trivial solution of the system is derived. Compared with the previous published results, our method does not resort to the Razumikhin‐type theorem and the semimartingale convergence theorem. Results of the development as presented in this paper are more general than those reported in some previously published papers. An illustrative example is also given to show the effectiveness of the obtained results.

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Keywords

Lyapunov function, Borel-Cantell's theory, exponential stability, stochastic stability, QA1-939, Cohen-Grossberg neural networks, Stochastic stability in control theory, Neural networks for/in biological studies, artificial life and related topics, Burkholder-Davids-Gundy inequality, Mathematics, Stochastic ordinary differential equations (aspects of stochastic analysis)

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
15
Top 10%
Top 10%
Top 10%
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