Robust exponential stabilization for uncertain neutral neural networks with interval time-varying delays by periodically intermittent control

2018 ◽  
Vol 32 (7) ◽  
pp. 2651-2664 ◽  
Author(s):  
Yali Dong ◽  
Liangliang Guo ◽  
Jing Hao
Keyword(s):  
Neural Networks ◽  
Exponential Stabilization ◽  
Intermittent Control ◽  
Time Varying ◽  
Interval Time ◽  
Periodically Intermittent Control ◽  
Robust Exponential Stabilization

scholarly journals Robust Exponential Stabilization of Stochastic Delay Interval Recurrent Neural Networks with Distributed Parameters and Markovian Jumping by Using Periodically Intermittent Control

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Junhao Hu ◽  
Yunjian Peng ◽  
Yan Li
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Sufficient Conditions ◽  
Exponential Stabilization ◽  
Intermittent Control ◽  
Markovian Jumping ◽  
Stochastic Delay ◽  
Periodically Intermittent Control ◽  
Distributed Parameters ◽  
Robust Exponential Stabilization

We consider a class of stochastic delay recurrent neural networks with distributed parameters and Markovian jumping. It is assumed that the coefficients in these neural networks belong to the interval matrices. Several sufficient conditions ensuring robust exponential stabilization are derived by using periodically intermittent control and Lyapunov functional. The obtained results are very easy to verify and implement, and improve the existing results. Finally, an example with numerical simulations is given to illustrate the presented criteria.

scholarly journals Exponential Synchronization of Inertial Cohen–Grossberg Neural Networks with Time-Varying Delays via Periodically Intermittent Control

2020 ◽  
Vol 15 (2) ◽  
pp. 15
Author(s):  
Qing Ding ◽  
Yinfang Song
Keyword(s):  
Neural Networks ◽  
Sufficient Conditions ◽  
Functional Method ◽  
Exponential Synchronization ◽  
Intermittent Control ◽  
Time Varying ◽  
Periodically Intermittent Control ◽  
Synchronization Problem ◽  
Control Schemes ◽  
Inequality Techniques

This paper deals with the exponential synchronization problem of inertial Cohen–Grossberg neural networks with time-varying delays under periodically intermittent control. In light of Lyapunov–Krasovskii functional method and inequality techniques, some sufficient conditions are attained to ensure the exponential synchronization of the master-slave system on the basis of p-norm. Meanwhile, the periodically intermittent control schemes are designed. Finally, in order to verify the effectiveness of theoretical results, some numerical simulations are provided.

Exponential synchronization for neural networks with mixed time-varying delays via periodically intermittent control

2014 ◽  
Vol 07 (02) ◽  
pp. 1450017
Author(s):  
Pinghua Yang ◽  
Xinan Tang
Keyword(s):  
Neural Networks ◽  
Lyapunov Functional ◽  
Exponential Synchronization ◽  
Intermittent Control ◽  
Time Varying ◽  
Periodically Intermittent Control ◽  
Analysis Techniques

This paper deals with exponential synchronization for a class of neural networks with mixed time-varying delays via periodically intermittent control. Some novel and effective exponential synchronization criteria are derived by constructing Lyapunov functional and applying some analysis techniques. These results presented in this paper generalize and improve many known results. Finally, this paper presents an illustrative example and uses the simulated results to show the feasibility and effectiveness of the proposed scheme.

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