Global asymptotic stability of fractional-order complex-valued neural networks with probabilistic time-varying delays

2021 ◽  
Author(s):  
Sihan Chen ◽  
Qiankun Song ◽  
Zhenjiang Zhao ◽  
Yurong Liu ◽  
Fuad E. Alsaadi
Keyword(s):  
Neural Networks ◽  
Asymptotic Stability ◽  
Fractional Order ◽  
Global Asymptotic Stability ◽  
Time Varying ◽  
Order Complex ◽  
Complex Valued

Global asymptotic stability of impulsive fractional-order complex-valued neural networks with time delay

2017 ◽  
Vol 243 ◽  
pp. 49-59 ◽  
Author(s):  
Limin Wang ◽  
Qiankun Song ◽  
Yurong Liu ◽  
Zhenjiang Zhao ◽  
Fuad E. Alsaadi
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Asymptotic Stability ◽  
Fractional Order ◽  
Global Asymptotic Stability ◽  
Order Complex ◽  
Complex Valued

Global Robust Synchronization of Fractional Order Complex Valued Neural Networks with Mixed Time Varying Delays and Impulses

2019 ◽  
Vol 17 (2) ◽  
pp. 509-520 ◽  
Author(s):  
Pratap Anbalagan ◽  
Raja Ramachandran ◽  
Jinde Cao ◽  
Grienggrai Rajchakit ◽  
Chee Peng Lim
Keyword(s):  
Neural Networks ◽  
Fractional Order ◽  
Time Varying ◽  
Order Complex ◽  
Robust Synchronization ◽  
Complex Valued

Finite-time Synchronization of fractional-order complex-valued fuzzy cellular neural networks with time-varying delays

2021 ◽  
pp. 1-11
Author(s):  
Wenbin Jin ◽  
Wenxia Cui ◽  
Zhenjie Wang
Keyword(s):  
Neural Networks ◽  
Fractional Order ◽  
Finite Time ◽  
Time Synchronization ◽  
Sufficient Conditions ◽  
Cellular Neural Networks ◽  
Time Varying ◽  
Order Complex ◽  
Fuzzy Cellular Neural Networks ◽  
Complex Valued

Finite-time synchronization is concerned for the fractional-order complex-valued fuzzy cellular neural networks (FOCVFCNNs) with leakage delay and time-varying delays. Without using the usual complex-valued system decomposition method, this paper designs the different forms of the controllers by using 2-norm. And we construct the appropriate Lyapunov functional and apply inequality analytical techniques, some new sufficient conditions are obtained to ensure finite-time synchronization of the FOCVFCNNs. The upper bound of setting-time function is obtained. Finally, numerical examples are examined to illustrate the effectiveness of the analytical results.

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