A new switching control for finite-time synchronization of memristor-based recurrent neural networks

2017 ◽  
Vol 86 ◽  
pp. 1-9 ◽  
Author(s):  
Jie Gao ◽  
Peiyong Zhu ◽  
Ahmed Alsaedi ◽  
Fuad E. Alsaadi ◽  
Tasawar Hayat
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Finite Time ◽  
Time Synchronization ◽  
Switching Control

Finite-time synchronization control of a class of memristor-based recurrent neural networks

2015 ◽  
Vol 63 ◽  
pp. 133-140 ◽  
Author(s):  
Minghui Jiang ◽  
Shuangtao Wang ◽  
Jun Mei ◽  
Yanjun Shen
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Finite Time ◽  
Time Synchronization ◽  
Synchronization Control

scholarly journals Finite-Time Synchronization for Complex-Valued Recurrent Neural Networks with Time Delays

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Ziye Zhang ◽  
Xiaoping Liu ◽  
Chong Lin ◽  
Bing Chen
Keyword(s):  
Neural Networks ◽  
Lyapunov Function ◽  
Recurrent Neural Networks ◽  
Finite Time ◽  
Time Delays ◽  
Time Synchronization ◽  
Great Difficulty ◽  
Activation Functions ◽  
Complex Valued ◽  
Independent Parameters

This paper focuses on the finite-time synchronization analysis for complex-valued recurrent neural networks with time delays. First, two kinds of common activation functions appearing in the existing references are combined together and more general assumptions are given. To achieve our aim, a nonlinear delayed controller with two independent parameters different from the existing ones is provided, which leads to great difficulty. To overcome it, a newly developed inequality is used. Then, via Lyapunov function approach, some criteria are derived to guarantee the finite-time synchronization of the considered system, and the settling time for synchronization is also estimated. Finally, two numerical simulations are given to support the effectiveness and advantages of the obtained results.

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