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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 3-4/2013

01.09.2013 | Original Article

Exponential stability of impulsive stochastic fuzzy cellular neural networks with mixed delays and reaction–diffusion terms

verfasst von: Guowei Yang, Yonggui Kao, Wei Li, Sun Xiqian

Erschienen in: Neural Computing and Applications | Ausgabe 3-4/2013

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Abstract

The problem of mean square exponential stability for a class of impulsive stochastic fuzzy cellular neural networks with distributed delays and reaction–diffusion terms is investigated in this paper. By using the properties of M-cone, eigenspace of the spectral radius of nonnegative matrices, Lyapunov functional, Itô’s formula and inequality techniques, several new sufficient conditions guaranteeing the mean square exponential stability of its equilibrium solution are obtained. The derived results are less conservative than the results recently presented in Wang and Xu (Chaos Solitons Fractals 42:2713–2721, 2009), Zhang and Li (Stability analysis of impulsive stochastic fuzzy cellular neural networks with time varying delays and reaction–diffusion terms. World Academy of Science, Engineering and Technology 2010), Huang (Chaos Solitons Fractals 31:658–664, 2007), and Wang (Chaos Solitons Fractals 38:878–885, 2008). In fact, the systems discussed in Wang and Xu (Chaos Solitons Fractals 42:2713–2721, 2009), Zhang and Li (Stability analysis of impulsive stochastic fuzzy cellular neural networks with time varying delays and reaction–diffusion terms. World Academy of Science, Engineering and Technology 2010), Huang (Chaos Solitons Fractals 31:658–664, 2007), and Wang (Chaos Solitons Fractals 38:878–885, 2008) are special cases of ours. Two examples are presented to illustrate the effectiveness and efficiency of the results.
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Metadaten
Titel
Exponential stability of impulsive stochastic fuzzy cellular neural networks with mixed delays and reaction–diffusion terms
verfasst von
Guowei Yang
Yonggui Kao
Wei Li
Sun Xiqian
Publikationsdatum
01.09.2013
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 3-4/2013
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-012-1040-0

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