New results on robust exponential stability of Takagi–Sugeno fuzzy for neutral differential systems with mixed time-varying delays

2021 ◽  
Author(s):  
Janejira Tranthi ◽  
Thongchai Botmart ◽  
Wajaree Weera ◽  
Teerapong La-inchua ◽  
Sirada Pinjai
Keyword(s):  
Exponential Stability ◽  
Time Varying ◽  
Robust Exponential Stability ◽  
Differential Systems ◽  
Takagi Sugeno

Robust Exponential Stability for Uncertain Discrete-Time Switched Systems with Interval Time-Varying Delay via a Switching Signal

2013 ◽  
Vol 479-480 ◽  
pp. 983-988
Author(s):  
Jenq Der Chen ◽  
Chang Hua Lien ◽  
Ker Wei Yu ◽  
Chin Tan Lee ◽  
Ruey Shin Chen ◽  
...  
Keyword(s):  
Exponential Stability ◽  
Discrete Time ◽  
Switched Systems ◽  
Time Varying ◽  
Signal Design ◽  
Robust Exponential Stability ◽  
Interval Time ◽  
Time Varying Delay ◽  
Switching Signal ◽  
Varying Delay

In this paper, the switching signal design to robust exponential stability for discrete-time switched systems with interval time-varying delay is considered. LMI-based conditions are proposed to guarantee the global exponential stability for such system with parametric perturbations by using a switching signal. The appropriate Lyapunov functionals are used to reduce the conservativeness of systems. Finally, a numerical example is illustrated to show the main results.

DELAY-DEPENDENT ROBUST EXPONENTIAL STABILITY FOR UNCERTAIN RECURRENT NEURAL NETWORKS WITH TIME-VARYING DELAYS

2007 ◽  
Vol 17 (03) ◽  
pp. 207-218 ◽  
Author(s):  
BAOYONG ZHANG ◽  
SHENGYUAN XU ◽  
YONGMIN LI
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Recurrent Neural Networks ◽  
Sufficient Conditions ◽  
Linear Matrix ◽  
Time Varying ◽  
Parameter Uncertainties ◽  
Robust Exponential Stability ◽  
Neuron Activation ◽  
Exponentially Stable

This paper considers the problem of robust exponential stability for a class of recurrent neural networks with time-varying delays and parameter uncertainties. The time delays are not necessarily differentiable and the uncertainties are assumed to be time-varying but norm-bounded. Sufficient conditions, which guarantee that the concerned uncertain delayed neural network is robustly, globally, exponentially stable for all admissible parameter uncertainties, are obtained under a weak assumption on the neuron activation functions. These conditions are dependent on the size of the time delay and expressed in terms of linear matrix inequalities. Numerical examples are provided to demonstrate the effectiveness and less conservatism of the proposed stability results.

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