New global robust stability condition for uncertain neural networks with time delays

2014 ◽  
Vol 142 ◽  
pp. 267-274 ◽  
Author(s):  
Neyir Ozcan ◽  
Sabri Arik
Keyword(s):  
Neural Networks ◽  
Robust Stability ◽  
Stability Condition ◽  
Time Delays ◽  
Global Robust Stability ◽  
Uncertain Neural Networks

scholarly journals Robust Stabilization andH∞Control for Uncertain Neural Networks with Mixed Time Delays

2015 ◽  
Vol 2015 ◽  
pp. 1-13
Author(s):  
Bin Wen ◽  
Hui Li ◽  
Li Liang
Keyword(s):  
Neural Networks ◽  
Robust Stability ◽  
Time Delays ◽  
Convex Combination ◽  
Robust Stabilization ◽  
Sufficient Conditions ◽  
Lyapunov Stability Theory ◽  
Linear Matrix ◽  
Mixed Time Delays ◽  
Uncertain Neural Networks

This paper is concerned with the problem of robust stabilization andH∞control for a class of uncertain neural networks. For the robust stabilization problem, sufficient conditions are derived based on the quadratic convex combination property together with Lyapunov stability theory. The feedback controller we design ensures the robust stability of uncertain neural networks with mixed time delays. We further design a robustH∞controller which guarantees the robust stability of the uncertain neural networks with a givenH∞performance level. The delay-dependent criteria are derived in terms of LMI (linear matrix inequality). Finally, numerical examples are provided to show the effectiveness of the obtained results.

Global robust stability of complex-valued recurrent neural networks with time-delays and uncertainties

2014 ◽  
Vol 07 (02) ◽  
pp. 1450016 ◽  
Author(s):  
Wei Zhang ◽  
Chuandong Li ◽  
Tingwen Huang
Keyword(s):  
Neural Networks ◽  
Robust Stability ◽  
Recurrent Neural Networks ◽  
Time Delays ◽  
Sufficient Conditions ◽  
Neural System ◽  
Multiple Time ◽  
Parameter Uncertainties ◽  
Global Robust Stability ◽  
Complex Valued

This paper focuses on the existence, uniqueness and global robust stability of equilibrium point for complex-valued recurrent neural networks with multiple time-delays and under parameter uncertainties with respect to two activation functions. Two sufficient conditions for robust stability of the considered neural networks are presented and established in two new time-independent relationships between the network parameters of the neural system. Finally, three illustrative examples are given to demonstrate the theoretical results.

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