Stability of Stochastic \(\theta \)-Methods for Stochastic Delay Hopfield Neural Networks Under Regime Switching
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.
stochastic \(\theta \)-methods stochastic delay hopfield neural networks regime switching gms-stability ms-stability