Abstract and Applied Analysis

Exponential Stability and Numerical Methods of Stochastic Recurrent Neural Networks with Delays

Shifang Kuang, Yunjian Peng, Feiqi Deng, and Wenhua Gao

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Abstract

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.

Article information

Source
Abstr. Appl. Anal., Volume 2013 (2013), Article ID 761237, 11 pages.

Dates
First available in Project Euclid: 27 February 2014

Permanent link to this document
https://projecteuclid.org/euclid.aaa/1393511999

Digital Object Identifier
doi:10.1155/2013/761237

Mathematical Reviews number (MathSciNet)
MR3090282

Zentralblatt MATH identifier
07095340

Citation

Kuang, Shifang; Peng, Yunjian; Deng, Feiqi; Gao, Wenhua. Exponential Stability and Numerical Methods of Stochastic Recurrent Neural Networks with Delays. Abstr. Appl. Anal. 2013 (2013), Article ID 761237, 11 pages. doi:10.1155/2013/761237. https://projecteuclid.org/euclid.aaa/1393511999


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