Stability of Stochastic \(\theta \)-Methods for Stochastic Delay Hopfield Neural Networks Under Regime Switching
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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.
KeywordsStochastic delay Hopfield neural networks Regime switching MS-stability GMS-stability Stochastic \(\theta \)-methods
The work is supported by the Fundamental Research Funds for the Central Universities, China Postdoctoral Science Foundation funded project under Grant 2012M511615 and the State Key Program of National Natural Science of China under Grant 61134012.
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