A new global robust stability condition for uncertain neural networks with discrete and distributed delays

2018 ◽  
Vol 10 (5) ◽  
pp. 1025-1035 ◽  
Author(s):  
Hao Chen ◽  
Wei Kang ◽  
Shouming Zhong
Keyword(s):  
Neural Networks ◽  
Robust Stability ◽  
Stability Condition ◽  
Distributed Delays ◽  
Global Robust Stability ◽  
Uncertain Neural Networks

A new global robust stability criteria for uncertain neural networks with fast time-varying delays

2008 ◽  
Vol 37 (2) ◽  
pp. 360-368 ◽  
Author(s):  
Jiqing Qiu ◽  
Jinhui Zhang ◽  
Jianfei Wang ◽  
Yuanqing Xia ◽  
Peng Shi
Keyword(s):  
Neural Networks ◽  
Robust Stability ◽  
Fast Time ◽  
Time Varying ◽  
Stability Criteria ◽  
Global Robust Stability ◽  
Uncertain Neural Networks

Robust stability analysis for delayed Cohen-Grossberg-type bidirectional associative memory neural networks with norm-bounded uncertainties

2009 ◽  
Vol 223 (5) ◽  
pp. 693-707
Author(s):  
Y Wang ◽  
P Hu
Keyword(s):  
Neural Networks ◽  
Associative Memory ◽  
Robust Stability ◽  
Sufficient Conditions ◽  
Linear Matrix ◽  
Bidirectional Associative Memory ◽  
Parameter Uncertainties ◽  
Robust Stability Analysis ◽  
Global Robust Stability ◽  
Bounded Uncertainties

In this paper, the problem of global robust stability is discussed for uncertain Cohen-Grossberg-type (CG-type) bidirectional associative memory (BAM) neural networks (NNs) with delays. The parameter uncertainties are supposed to be norm bounded. The sufficient conditions for global robust stability are derived by employing a Lyapunov-Krasovskii functional. Based on these, the conditions ensuring global asymptotic stability without parameter uncertainties are established. All conditions are expressed in terms of linear matrix inequalities (LMIs). In addition, two examples are provided to illustrate the effectiveness of the results obtained.

Sign in / Sign up

Export Citation Format

Share Document