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Exponential Stability and Numerical Methods of Stochastic Recurrent Neural Networks with Delays
Joint Authors
Deng, Feiqi
Peng, Yunjian
Gao, Wenhua
Kuang, Shifang
Source
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-08-06
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Kuang, Shifang& Peng, Yunjian& Deng, Feiqi& Gao, Wenhua. 2013. Exponential Stability and Numerical Methods of Stochastic Recurrent Neural Networks with Delays. Abstract and Applied Analysis،Vol. 2013, no. 2013, pp.1-11.
https://search.emarefa.net/detail/BIM-496671
Modern Language Association (MLA)
Kuang, Shifang…[et al.]. Exponential Stability and Numerical Methods of Stochastic Recurrent Neural Networks with Delays. Abstract and Applied Analysis No. 2013 (2013), pp.1-11.
https://search.emarefa.net/detail/BIM-496671
American Medical Association (AMA)
Kuang, Shifang& Peng, Yunjian& Deng, Feiqi& Gao, Wenhua. Exponential Stability and Numerical Methods of Stochastic Recurrent Neural Networks with Delays. Abstract and Applied Analysis. 2013. Vol. 2013, no. 2013, pp.1-11.
https://search.emarefa.net/detail/BIM-496671
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-496671