Reliable filter design for discrete-time neural networks with Markovian jumping parameters and time-varying delay

2020 ◽  
Vol 357 (5) ◽  
pp. 2892-2915 ◽  
Author(s):  
Weifeng Xia ◽  
Shengyuan Xu ◽  
Junwei Lu ◽  
Zhengqiang Zhang ◽  
Yuming Chu
Keyword(s):  
Neural Networks ◽  
Discrete Time ◽  
Filter Design ◽  
Time Varying ◽  
Markovian Jumping Parameters ◽  
Markovian Jumping ◽  
Time Varying Delay ◽  
Varying Delay

ROBUST EXPONENTIAL STABILITY OF MARKOVIAN JUMPING NEURAL NETWORKS WITH TIME-VARYING DELAY

2008 ◽  
Vol 18 (03) ◽  
pp. 207-218 ◽  
Author(s):  
MING GAO ◽  
XUYANG LOU ◽  
BAOTONG CUI
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Global Exponential Stability ◽  
Sufficient Condition ◽  
Time Varying ◽  
Markovian Jumping Parameters ◽  
Markovian Jumping ◽  
Robust Exponential Stability ◽  
Time Varying Delay ◽  
Varying Delay

This paper considers the robust stability of a class of neural networks with Markovian jumping parameters and time-varying delay. By employing a new Lyapunov–Krasovskii functional, a sufficient condition for the global exponential stability of the delayed Markovian jumping neural networks is established. The proposed condition is also extended to the uncertain cases, which are shown to be the improvement and extension of the existing ones. Finally, the validity of the results are illustrated by an example.

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