Open AccessStochastic Dynamics of Nonautonomous Cohen-Grossberg Neural Networks
Author(s) -
Chuangxia Huang,
Jinde Cao
Publication year - 2011
Publication title -
abstract and applied analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.228
H-Index - 56
eISSN - 1687-0409
pISSN - 1085-3375
DOI - 10.1155/2011/297147
Subject(s) - semimartingale , mathematics , convergence (economics) , moment (physics) , artificial neural network , stability (learning theory) , exponential stability , lyapunov function , function (biology) , pure mathematics , mathematical economics , artificial intelligence , machine learning , computer science , physics , classical mechanics , nonlinear system , quantum mechanics , evolutionary biology , economics , biology , economic growth
This paper is devoted to the study of the stochastic stability of a class ofCohen-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 solutionof the system is derived. Compared with the previous published results, our methoddoes not resort to the Razumikhin-type theorem and the semimartingale convergencetheorem. Results of the development as presented in this paper are more general thanthose reported in some previously published papers. An illustrative example is alsogiven to show the effectiveness of the obtained results
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