Event-triggered impulsive control on quasi-synchronization of memristive neural networks with time-varying delays

2019 ◽  
Vol 110 ◽  
pp. 55-65 ◽  
Author(s):  
Yufeng Zhou ◽  
Zhigang Zeng
Keyword(s):  
Neural Networks ◽  
Impulsive Control ◽  
Time Varying ◽  
Memristive Neural Networks ◽  
Event Triggered

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 Stochastic Exponential Stabilization for Markov Jump Neural Networks with Time-varying Delays via Adaptive Event-Triggered Impulsive Control

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Xiaoman Liu ◽  
Haiyang Zhang ◽  
Tao Wu ◽  
Jinlong Shu
Keyword(s):  
Neural Networks ◽  
Impulsive Control ◽  
Exponential Stabilization ◽  
Linear Matrix ◽  
Time Varying ◽  
Matrix Inequalities ◽  
Control Cost ◽  
Markov Jump ◽  
Type Integral ◽  
Event Triggered

This paper focuses on the exponential stabilization problem for Markov jump neural networks with Time-varying Delays (TDs). Firstly, we provide a new Free-matrix-based Exponential-type Integral Inequality (FMEII) containing the information of attenuation exponent, which is helpful to reduce the conservativeness of stability criteria. To further save control cost, we introduce a sample-based Adaptive Event-triggered Impulsive Control (AEIC) scheme, in which the trigger threshold is adaptively varied with the sampled state. By fully considering the information about sampled state, TDs, and Markov jump parameters, a suitable Lyapunov–Krasovskii functional is constructed. With the virtue of FMEII and AEIC scheme, some novel stabilization criteria are presented in the form of linear matrix inequalities. At last, two numerical examples are given to show the validity of the obtained results.

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.

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