Exponential Synchronization of Stochastic Neural Networks with Time-Varying Delays and Lévy Noises via Event-Triggered Control

2021 ◽  
Author(s):  
Danni Lu ◽  
Dongbing Tong ◽  
Qiaoyu Chen ◽  
Wuneng Zhou ◽  
Jun Zhou ◽  
...  
Keyword(s):  
Neural Networks ◽  
Exponential Synchronization ◽  
Stochastic Neural Networks ◽  
Time Varying ◽  
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 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 Stability for Stochastic Neural Networks with Time-Varying Delay and Leakage Delay

2015 ◽  
Vol 742 ◽  
pp. 399-403
Author(s):  
Ya Jun Li ◽  
Jing Zhao Li
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Sufficient Conditions ◽  
Leakage Delay ◽  
Linear Matrix ◽  
Stochastic Neural Networks ◽  
Time Varying ◽  
Matrix Inequalities ◽  
Time Varying Delay ◽  
Varying Delay

This paper investigates the exponential stability problem for a class of stochastic neural networks with leakage delay. By employing a suitable Lyapunov functional and stochastic stability theory technic, the sufficient conditions which make the stochastic neural networks system exponential mean square stable are proposed and proved. All results are expressed in terms of linear matrix inequalities (LMIs). Example and simulation are presented to show the effectiveness of the proposed method.

Robust stability analysis of stochastic neural networks with Markovian jumping parameters and probabilistic time-varying delays

Complexity ◽  
2014 ◽  
Vol 21 (5) ◽  
pp. 59-72 ◽  
Author(s):  
Chandrasekar Pradeep ◽  
Arunachalam Chandrasekar ◽  
Rangasamy Murugesu ◽  
Rajan Rakkiyappan
Keyword(s):  
Neural Networks ◽  
Stability Analysis ◽  
Robust Stability ◽  
Stochastic Neural Networks ◽  
Time Varying ◽  
Markovian Jumping Parameters ◽  
Markovian Jumping ◽  
Robust Stability Analysis
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