Global dissipativity in the mean square of stochastic Cohen-Grossberg neural networks with time delays

In this paper, the problem of global dissipativity in the mean square is discussed for stochastic Cohen-Grossberg neural networks with time delays. By constructing general Lyapunov functions, combining with Itô's formula, several sufficient conditions for the global dissipativity in the mean s...

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Bibliographic Details
Published in2017 29th Chinese Control And Decision Conference (CCDC) pp. 2487 - 2491
Main Authors Liangliang Li, Wenlin Jiang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2017
Subjects
attractive set
Delay effects
dissipativity in the mean square
Electronic mail
Itô's formula
Lyapunov methods
Neural networks
Neurons
Stability criteria
Stochastic Cohen-Grossberg neural networks
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Summary:In this paper, the problem of global dissipativity in the mean square is discussed for stochastic Cohen-Grossberg neural networks with time delays. By constructing general Lyapunov functions, combining with Itô's formula, several sufficient conditions for the global dissipativity in the mean square are derived. Moreover, we give out the estimations of globally attractive sets. Finally, one example is given to show the effectiveness of the proposed criteria.
ISSN:1948-9447
DOI:10.1109/CCDC.2017.7978932