DocumentCode
2954034
Title
Asymptotic behavior of stochastic Cohen-Grossberg neural networks with variable delays
Author
Jiang, Minghui ; Wang, Li ; Shen, Yi
Author_Institution
Coll. of Sci., China Three Gorges Univ., Yichang
fYear
2008
fDate
1-8 June 2008
Firstpage
279
Lastpage
284
Abstract
Using Chebyshev inequality and nonnegative semi-martingale convergence theorem, the paper investigates asymptotic behavior of stochastic Cohen-Grossberg neural networks with delay by constructing suitable Lyapunov functional. Algebraic criteria are given for stochastic ultimate bounded and almost exponential stability. The result in the paper extend the main conclusion. In the end, examples are given to verify the effective of our results.
Keywords
Lyapunov methods; asymptotic stability; convergence; delays; neural nets; stochastic processes; Chebyshev inequality; Lyapunov functional; asymptotic behavior; nonnegative semi-martingale convergence theorem; stochastic Cohen-Grossberg neural network; variable delay; Artificial neural networks; Chebyshev approximation; Convergence; Differential equations; Neural networks; Neurons; Stability; Stochastic processes; Stochastic resonance; Working environment noise;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on
Conference_Location
Hong Kong
ISSN
1098-7576
Print_ISBN
978-1-4244-1820-6
Electronic_ISBN
1098-7576
Type
conf
DOI
10.1109/IJCNN.2008.4633803
Filename
4633803
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