Improve results on robustness analysis for connection weight matrices of global exponential stability of stochastic recurrent neural networks

In this paper, we obtain improve results,which on robustness analysis of global exponential stability of stochastic recurrent neural networks(SRNNs) subjected to parameter uncertainty in connection weight matrices. Novel exponential stability criteria for the RNNs are derived, which upper bounds of connection weight matrices uncertainty are characterized by solving transcendental equations containing adjustable parameters. Through the selection of the adjustable parameters, the upper bounds are improved. It shows that our results generalize and improve the corresponding results of recent works. In addition, a numerical example is given to show the effectiveness of the results we obtained.