Synchronisation of multiple neural networks via event‐triggered time‐varying delay hybrid impulsive control

2021 ◽  
Author(s):  
Xiaoli Ruan ◽  
Chen Xu ◽  
Jianwen Feng ◽  
Jingyi Wang ◽  
Yi Zhao
Keyword(s):  
Neural Networks ◽  
Impulsive Control ◽  
Time Varying ◽  
Time Varying Delay ◽  
Multiple Neural Networks ◽  
Varying Delay ◽  
Event Triggered

scholarly journals Adaptive Event-Triggered Synchronization of Networked Neural Networks with Time-Varying Delay Subject to Actuator Saturation

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Yao Xu ◽  
Renren Wang ◽  
Hongqian Lu ◽  
Xingxing Song ◽  
Yahan Deng ◽  
...  
Keyword(s):  
Neural Networks ◽  
Actuator Saturation ◽  
Synchronization Error ◽  
Linear Matrix ◽  
Time Varying ◽  
Time Varying Delay ◽  
Network Utilization ◽  
The Stability ◽  
Varying Delay ◽  
Event Triggered

This paper discusses the adaptive event-triggered synchronization problem of a class of neural networks (NNs) with time-varying delay and actuator saturation. First, in view of the limited communication channel capacity of the network system and unnecessary data transmission in the NCSs, an adaptive event-triggered scheme (AETS) is introduced to reduce the network load and improve network utilization. Second, under the AETS, the synchronization error model of the delayed master-slave synchronization system is constructed with actuator saturation. Third, based on Lyapunov–Krasovskii functional (LKF), a new sufficient criterion to guarantee the asymptotic stability of the synchronization error system is derived. Moreover, by solving the stability criterion expressed in the form of a set of linear matrix inequalities (LMIs), some necessary parameters of the system are obtained. At last, two examples are expressed to demonstrate the feasibility of this method.

scholarly journals Improved Stability Criteria of Static Recurrent Neural Networks with a Time-Varying Delay

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Lei Ding ◽  
Hong-Bing Zeng ◽  
Wei Wang ◽  
Fei Yu
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Linear Matrix ◽  
Time Varying ◽  
Time Varying Delay ◽  
Improved Stability ◽  
The Stability ◽  
Delay Dependent ◽  
Delay Dependent Stability ◽  
Varying Delay

This paper investigates the stability of static recurrent neural networks (SRNNs) with a time-varying delay. Based on the complete delay-decomposing approach and quadratic separation framework, a novel Lyapunov-Krasovskii functional is constructed. By employing a reciprocally convex technique to consider the relationship between the time-varying delay and its varying interval, some improved delay-dependent stability conditions are presented in terms of linear matrix inequalities (LMIs). Finally, a numerical example is provided to show the merits and the effectiveness of the proposed methods.

Sign in / Sign up

Export Citation Format

Share Document