Robust exponential stability analysis of discrete-time switched Hopfield neural networks with time delay

2011 ◽  
Vol 5 (3) ◽  
pp. 525-534 ◽  
Author(s):  
Linlin Hou ◽  
Guangdeng Zong ◽  
Yuqiang Wu
Keyword(s):  
Neural Networks ◽  
Stability Analysis ◽  
Time Delay ◽  
Exponential Stability ◽  
Discrete Time ◽  
Hopfield Neural Networks ◽  
Robust Exponential Stability

Robust Exponential Stability Analysis for Interval Neural Networks with Time Delay

2011 ◽  
Vol 58-60 ◽  
pp. 2597-2601
Author(s):  
Shou Yi Qian ◽  
Li Xie
Keyword(s):  
Neural Networks ◽  
Stability Analysis ◽  
Time Delay ◽  
Exponential Stability ◽  
Lyapunov Stability Theory ◽  
Linear Matrix ◽  
Matrix Transformations ◽  
Parameter Uncertainties ◽  
Robust Exponential Stability ◽  
Interval Neural Networks

The problem of robust exponential stability analysis for nonlinear uncertain interval neural networks with time delay is investigated. The nonlinear uncertainties are assumed to satisfy the cone constraint conditions. The interval parameters of the neural networks are equivalent to norm matched parameter uncertainties via some matrix transformations. The stable criteria for the uncertain interval neural networks with time delays are developed by use of the Lyapunov stability theory. All the stability conditions in this paper are presented in terms of linear matrix inequalities.

scholarly journals On Global Exponential Stability of Discrete-Time Hopfield Neural Networks with Variable Delays

2007 ◽  
Vol 2007 ◽  
pp. 1-9 ◽  
Author(s):  
Qiang Zhang ◽  
Xiaopeng Wei ◽  
Jin Xu
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Discrete Time ◽  
Global Exponential Stability ◽  
Stability Result ◽  
Hopfield Neural Networks ◽  
Difference Inequality ◽  
Variable Delays

Global exponential stability of a class of discrete-time Hopfield neural networks with variable delays is considered. By making use of a difference inequality, a new global exponential stability result is provided. The result only requires the delay to be bounded. For this reason, the result is milder than those presented in the earlier references. Furthermore, two examples are given to show the efficiency of our result.

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