Event-Triggered Exponential Synchronization for Complex-Valued Memristive Neural Networks With Time-Varying Delays

2020 ◽  
Vol 31 (10) ◽  
pp. 4104-4116 ◽  
Author(s):  
Xiaofan Li ◽  
Wenbing Zhang ◽  
Jian-An Fang ◽  
Huiyuan Li
Keyword(s):  
Neural Networks ◽  
Exponential Synchronization ◽  
Time Varying ◽  
Memristive Neural Networks ◽  
Complex Valued ◽  
Event Triggered

scholarly journals Exponential Synchronization of Memristive Neural Networks with Discrete and Distributed Time-Varying Delays via Event-Triggered Control

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Biwen Li ◽  
Wenbo Zhou
Keyword(s):  
Neural Networks ◽  
Exponential Synchronization ◽  
Time Varying ◽  
Memristive Neural Networks ◽  
Dynamic Event ◽  
Synchronization Problem ◽  
Event Triggering ◽  
Triggering Conditions ◽  
Theoretical Results ◽  
Event Triggered

In this paper, we investigate the exponential synchronization problem of memristive neural networks (MNNs) with discrete and distributed time-varying delays under event-triggered control. An event-triggered controller with the static and dynamic event-triggering conditions is designed to improve the efficiency of resource utilization. By constructing a new Lyapunov function, some sufficient criteria are obtained to realize the exponential synchronization of considered drive-response MNNs under the designed event-triggered controller. In addition, the Zeno behavior will not occur by proving that the event-triggering interval has a positive lower bound under different event-triggering conditions. Finally, a numerical example is provided to prove the validity of our theoretical results.

Exponential synchronization of memristive neural networks with time-varying delays via quantized sliding-mode control

2020 ◽  
Vol 126 ◽  
pp. 163-169
Author(s):  
Bo Sun ◽  
Shengbo Wang ◽  
Yuting Cao ◽  
Zhenyuan Guo ◽  
Tingwen Huang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Sliding Mode Control ◽  
Sliding Mode ◽  
Exponential Synchronization ◽  
Time Varying ◽  
Memristive Neural Networks ◽  
Mode Control

scholarly journals Synchronization of inertial memristive neural networks with time-varying delays via static or dynamic event-triggered control

2020 ◽  
Vol 404 ◽  
pp. 367-380 ◽  
Author(s):  
Wei Yao ◽  
Chunhua Wang ◽  
Yichuang Sun ◽  
Chao Zhou ◽  
Hairong Lin
Keyword(s):  
Neural Networks ◽  
Time Varying ◽  
Memristive Neural Networks ◽  
Dynamic Event ◽  
Event Triggered

scholarly journals Stochastically exponential synchronization for Markov jump neural networks with time-varying delays via event-triggered control scheme

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Xiaoman Liu ◽  
Haiyang Zhang ◽  
Jun Yang ◽  
Hao Chen
Keyword(s):  
Neural Networks ◽  
Convex Combination ◽  
Analytical Techniques ◽  
Exponential Synchronization ◽  
Time Varying ◽  
Markov Jump ◽  
Synchronization Problem ◽  
Control Scheme ◽  
Reciprocally Convex Combination ◽  
Event Triggered

AbstractThis paper focuses on the stochastically exponential synchronization problem for one class of neural networks with time-varying delays (TDs) and Markov jump parameters (MJPs). To derive a tighter bound of reciprocally convex quadratic terms, we provide an improved reciprocally convex combination inequality (RCCI), which includes some existing ones as its particular cases. We construct an eligible stochastic Lyapunov–Krasovskii functional to capture more information about TDs, triggering signals, and MJPs. Based on a well-designed event-triggered control scheme, we derive several novel stability criteria for the underlying systems by employing the new RCCI and other analytical techniques. Finally, we present two numerical examples to show the validity of our methods.

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