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.

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.

scholarly journals Adaptive Exponential Synchronization for Stochastic Competitive Neural Networks with Time-Varying Leakage Delays and Reaction-Diffusion Terms

2017 ◽  
Vol 2017 ◽  
pp. 1-22 ◽  
Author(s):  
Zhiqiang Wang ◽  
Lili Wang ◽  
Rui Xu
Keyword(s):  
Neural Networks ◽  
Reaction Diffusion ◽  
Diffusion Time ◽  
Exponential Synchronization ◽  
Spatial Diffusion ◽  
Time Varying ◽  
Adaptive Feedback ◽  
Leakage Delays ◽  
Synchronization Problem ◽  
Theoretical Results

We study the exponential synchronization problem for a class of stochastic competitive neural networks with different timescales, as well as spatial diffusion, time-varying leakage delays, and discrete and distributed time-varying delays. By introducing several important inequalities and using Lyapunov functional technique, an adaptive feedback controller is designed to realize the exponential synchronization for the proposed competitive neural networks in terms of p-norm. According to the theoretical results obtained in this paper, the influences of the timescale, external stimulus constants, disposable scaling constants, and controller parameters on synchronization are analyzed. Numerical simulations are presented to show the feasibility of the theoretical results.

scholarly journals Adaptive Aperiodically Intermittent Synchronization for Complex Dynamical Network with Unknown Time-Varying Outer Coupling Strengths

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Lihong Yan ◽  
Junmin Li
Keyword(s):  
Adaptive Learning ◽  
Exponential Synchronization ◽  
Intermittent Control ◽  
Time Varying ◽  
Complex Dynamical Network ◽  
Dynamical Network ◽  
Synchronization Problem ◽  
Coupling Strengths ◽  
Inequality Techniques ◽  
Theoretical Results

In this paper, exponential synchronization problem of complex dynamical networks with unknown periodically coupling strengths was investigated. An aperiodically intermittent control synchronization strategy is proposed. Based on Lyapunov exponential stability theory, inequality techniques, and adaptive learning laws design, some sufficient exponential synchronization criteria for complex dynamical network with unknown periodical coupling weights are obtained. The numerical simulation example is presented to illustrate the feasibility of theoretical results.

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