scholarly journals Robustness analysis of global exponential stability in neural networks evoked by deviating argument and stochastic disturbance

2017 ◽  
Vol 10 (11) ◽  
pp. 5646-5667
Author(s):  
Liguang Wan ◽  
Ailong Wu ◽  
Jingru Chen
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Global Exponential Stability ◽  
Robustness Analysis ◽  
Stochastic Disturbance ◽  
Deviating Argument

scholarly journals Robust Exponential Stability of Recurrent Neural Networks with Deviating Argument and Stochastic Disturbance

2020 ◽  
Vol 13 (4) ◽  
pp. 794-806
Author(s):  
Zha Mingxin ◽  
Si Wenxiao ◽  
Xie Tao
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Recurrent Neural Networks ◽  
Robust Exponential Stability ◽  
Stochastic Disturbance ◽  
Deviating Argument ◽  
Know How ◽  
Exponentially Stable ◽  
The Stability ◽  
Upper Boundary

It is well known that deviating argument and stochastic disturbance may derail the stability of recurrent neural networks (RNNs). This paper discusses the robustness of global exponential stability (GES) of RNNs accompanied with deviating argument and stochastic disturbance. For a given global exponentially stable RNNs, it is interesting to know how much the length of the interval of piecewise function and the interference intensity so that the disturbed system may still be exponentially stable. The available upper boundary of the range of piecewise variables and the interference intensity in the disturbed RNNs to keep GES are the solutions of some transcendental equations. Finally, some examples are provided to demonstrate the efficacy of the inferential results.

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