scholarly journals Lag Synchronization of Coupled Delayed Chaotic Neural Networks by Periodically Intermittent Control

2013 ◽  
Vol 2013 ◽  
pp. 1-5 ◽  
Author(s):  
Songjian Dan ◽  
Simon X. Yang ◽  
Wei Feng
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Sufficient Conditions ◽  
Lyapunov Stability Theory ◽  
Intermittent Control ◽  
Lag Synchronization ◽  
Chaotic Neural Networks ◽  
Coupled Neural Networks ◽  
Error System ◽  
Delayed Chaotic Neural Networks

We investigate the lag synchronization of coupled neural networks with time delay. Some sufficient conditions for lag synchronization will be derived by using Lyapunov stability theory and intermittent control. Compared to existing results, some less conservative conditions are derived to guarantee the stabilization of error system. The analytical results are confirmed by numerical simulations.

scholarly journals Projective Lag Synchronization of Delayed Neural Networks Using Intermittent Linear State Feedback

2013 ◽  
Vol 2013 ◽  
pp. 1-5 ◽  
Author(s):  
Junjian Huang ◽  
Chuandong Li ◽  
Tingwen Huang ◽  
Huaqing Li ◽  
Mei Peng
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
State Feedback ◽  
Lyapunov Stability Theory ◽  
Neural Systems ◽  
Lag Synchronization ◽  
Delayed Neural Networks ◽  
Coupled Neural Networks ◽  
Projective Lag Synchronization ◽  
Linear State

The problem of projective lag synchronization of coupled neural networks with time delay is investigated. By means of the Lyapunov stability theory, an intermittent controller is designed for achieving projective lag synchronization between two delayed neural networks systems. Numerical simulations on coupled Lu neural systems illustrate the effectiveness of the results.

scholarly journals Synchronization of Time Delay Coupled Neural Networks Based on Impulsive Control

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Jie Fang ◽  
Yin Zhang ◽  
Danying Xu ◽  
Junwei Sun
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Control Method ◽  
Impulsive Control ◽  
Lyapunov Stability Theory ◽  
Control Effect ◽  
Coupled Neural Networks ◽  
Impulsive Synchronization ◽  
The Stability ◽  
Delayed Impulses

This paper investigates the impulsive synchronization of time delay coupled neural networks. Based on the Lyapunov stability theory and impulsive control method, a distributed delayed impulsive controller is designed to realize synchronization of the coupled neural networks. A new impulsive delayed inequality is proposed, where the control effect of distributed delayed impulses is fully considered. In addition, a suitable Lyapunov-like function is established to prove the stability of the synchronization system. Numerical simulation examples are introduced to illustrate the effectiveness and feasibility of the main results.

scholarly journals Lag Synchronization of Hyperchaotic Systems via Intermittent Control

2012 ◽  
Vol 2012 ◽  
pp. 1-10
Author(s):  
Junjian Huang ◽  
Chuandong Li ◽  
Wei Zhang ◽  
Pengcheng Wei ◽  
Qi Han
Keyword(s):  
Theoretical Analysis ◽  
Lyapunov Stability ◽  
Stability Theory ◽  
Sufficient Conditions ◽  
Lyapunov Stability Theory ◽  
Intermittent Control ◽  
Lag Synchronization ◽  
Control Scheme ◽  
Hyperchaotic Systems ◽  
Theoretical Results

Different from the most existing results, in this paper an intermittent control scheme is designed to achieve lag synchronization of coupled hyperchaotic systems. Several sufficient conditions ensuring lag synchronization are proposed by rigorous theoretical analysis with the help of the Lyapunov stability theory. Numerical simulations are also presented to show the effectiveness and feasibility of the theoretical results.

Synchronization of coupled reaction–diffusion neural networks via intermittent control and saturated impulses

2021 ◽  
pp. 2150398
Author(s):  
Zhengran Cao ◽  
Chuandong Li ◽  
Zhilong He ◽  
Xiaoyu Zhang
Keyword(s):  
Neural Networks ◽  
Actuator Saturation ◽  
Impulsive Control ◽  
Input Saturation ◽  
Sufficient Conditions ◽  
Reaction Diffusion ◽  
Linear Matrix ◽  
Intermittent Control ◽  
Coupled Neural Networks ◽  
Hybrid Controller

The impulsive synchronization of coupled neural networks with input saturation and the term of reaction–diffusion via a hybrid control strategy is investigated. In this paper, a hybrid controller is proposed, including impulsive controller with input saturation and intermittent controller. This type of hybrid controller can not only solve the periodic and aperiodic intermittent control, lower the update frequency of the controller, but also avoid the saturation phenomenon of impulsive control. Based on linear matrix inequalities (LMIs), and Jensen’s inequality, under a proposed suitable Lyapunov function, a series of sufficient conditions are established to guarantee the stability of the error system. Compared with the recent relevant impulsive saturation results, the polytopic representation method dealing with actuator saturation may make the synchronization criterion more universal and less restrictive. Finally, a numerical example is provided to verify the correctness and feasibility of the theoretical results.

scholarly journals Finite time Synchronization of Inertial Memristive Neural Networks with Time Varying Delay

2021 ◽  
Vol 16 (4) ◽  
Author(s):  
Yajing Pang ◽  
Shengmei Dong
Keyword(s):  
Neural Networks ◽  
Finite Time ◽  
Time Synchronization ◽  
Sufficient Conditions ◽  
Lyapunov Stability Theory ◽  
Fixed Time ◽  
Memristive Neural Networks ◽  
Time Varying Delay ◽  
Error System ◽  
Varying Delay

Finite time synchronization control of inertial memristor-based neural networks with varying delay is considered. In view of drive and response concept, the sufficient conditions to ensure finite time synchronization issue of inertial memristive neural networks is given. Based on Lyapunov finite time asymptotic theory, a kind of feedback controllers is designed for inertial memristorbased neural networks to realize the finite time synchronization. Based on Lyapunov stability theory, close loop error system can be proved finite time and fixed time stable. Finally, illustrative example is given to illustrate the effectiveness of theoretical results.

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