Stability of Stochastic $$\theta $$-Methods for Stochastic Delay Hopfield Neural Networks Under Regime Switching

Authors:
Feng Jiang;Yi Shen
Affiliations:
School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan, China 430073;Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan, China 430074
Venue:
Neural Processing Letters
Year:
2013

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Abstract

This paper is concerned with the general mean-square (GMS) stability and mean-square (MS) stability of stochastic $$\theta $$-methods for stochastic delay Hopfield neural networks under regime switching. The sufficient conditions to guarantee GMS-stability and MS-stability of stochastic $$\theta $$-methods are given. Finally, an example is used to illustrate the effectiveness of our result.