Stability Analysis of Memristor Neural Networks with State-Dependent Delay

2021 ◽  
Author(s):  
Yue Chen ◽  
Song Zhu
Keyword(s):  
Neural Networks ◽  
Stability Analysis ◽  
State Dependent ◽  
State Dependent Delay

scholarly journals Anti-periodic behavior for quaternion-valued delayed cellular neural networks

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Zhenhua Duan ◽  
Changjin Xu
Keyword(s):  
Neural Networks ◽  
Periodic Solutions ◽  
Coincidence Degree ◽  
Distributed Delay ◽  
Degree Theory ◽  
Cellular Neural Networks ◽  
Sufficient Condition ◽  
State Dependent ◽  
State Dependent Delay ◽  
Inequality Techniques

AbstractIn this manuscript, quaternion-valued delayed cellular neural networks are studied. Applying the continuation theorem of coincidence degree theory, inequality techniques and a Lyapunov function approach, a new sufficient condition that guarantees the existence and exponential stability of anti-periodic solutions for quaternion-valued delayed cellular neural networks is presented. The obtained results supplement some earlier publications that deal with the anti-periodic solutions of quaternion-valued neural networks with distributed delay or impulse or state-dependent delay or inertial term. Computer simulations are displayed to check the derived analytical results.

scholarly journals Stability Analysis of a Class of Neural Networks with State-Dependent State Delay

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Yue Chen ◽  
Jin-E Zhang
Keyword(s):  
Neural Networks ◽  
Sufficient Conditions ◽  
Zero Solution ◽  
Time Dependent ◽  
State Delay ◽  
State Dependent ◽  
State Dependent Delay ◽  
The Difference ◽  
The Stability ◽  
Dependent State

The differential equations with state-dependent delay are very important equations because they can describe some problems in the real world more accurately. Due to the complexity of state-dependent delay, it also brings challenges to the research. The value of delay varying with the state is the difference between state-dependent delay and time-dependent delay. It is impossible to know exactly in advance how far historical state information is needed, and then the problem of state-dependent delay is more complicated compared with time-dependent delay. The dominating work of this paper is to solve the stability problem of neural networks equipped with state-dependent state delay. We use the purely analytical method to deduce the sufficient conditions for local exponential stability of the zero solution. Finally, a few numerical examples are presented to prove the availability of our results.

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