Open AccessExponential Stability and Numerical Methods of Stochastic Recurrent Neural Networks with Delays
Author(s) -
Shifang Kuang,
Yunjian Peng,
Feiqi Deng,
Wenhua Gao
Publication year - 2013
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 - 10.1155/2013/761237
Subject(s) - mathematics , mean square , stability (learning theory) , exponential stability , euler's formula , exponential function , square (algebra) , mathematical analysis , computer science , geometry , nonlinear system , machine learning , quantum mechanics , physics
Exponential stability in mean square of stochastic delay recurrent neural networks is investigated in detail. By using Itô’s formula and inequality techniques, the sufficient conditions to guarantee the exponential stability in mean square of an equilibrium are given. Under the conditions which guarantee the stability of the analytical solution, the Euler-Maruyama scheme and the split-step backward Euler scheme are proved to be mean-square stable. At last, an example is given to demonstrate our results
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