Global Mittag-Leffler stability and synchronization of discrete-time fractional-order complex-valued neural networks with time delay

2020 ◽  
Vol 122 ◽  
pp. 382-394 ◽  
Author(s):  
Xingxing You ◽  
Qiankun Song ◽  
Zhenjiang Zhao
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Fractional Order ◽  
Discrete Time ◽  
Order Complex ◽  
Complex Valued

Dissipativity and stability analysis of fractional-order complex-valued neural networks with time delay

2017 ◽  
Vol 86 ◽  
pp. 42-53 ◽  
Author(s):  
G. Velmurugan ◽  
R. Rakkiyappan ◽  
V. Vembarasan ◽  
Jinde Cao ◽  
Ahmed Alsaedi
Keyword(s):  
Neural Networks ◽  
Stability Analysis ◽  
Time Delay ◽  
Fractional Order ◽  
Order Complex ◽  
Complex Valued

scholarly journals Stability and Hopf Bifurcation of Fractional-Order Complex-Valued Neural Networks With Time-Delay

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 158798-158807 ◽  
Author(s):  
Xiaohong Wang ◽  
Zhen Wang ◽  
Xianggeng Zhu ◽  
Bo Meng ◽  
Jianwei Xia
Keyword(s):  
Neural Networks ◽  
Hopf Bifurcation ◽  
Time Delay ◽  
Fractional Order ◽  
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

scholarly journals Robust Asymptotical Stability and Stabilization of Fractional-Order Complex-Valued Neural Networks with Delay

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Jingjing Zeng ◽  
Xujun Yang ◽  
Lu Wang ◽  
Xiaofeng Chen
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Fractional Order ◽  
Asymptotical Stability ◽  
Order Complex ◽  
Practical Application ◽  
Stability And Stabilization ◽  
Complex Valued ◽  
Fractional Orders ◽  
Theoretical Results

The robust asymptotical stability and stabilization for a class of fractional-order complex-valued neural networks (FCNNs) with parametric uncertainties and time delay are considered in this paper. It is worth noting that our system combines complex numbers, uncertain parameters, time delay, and fractional orders, which is universal in practical application. Using the theorem of homeomorphism, the sufficient condition of the existence and uniqueness of the equilibrium point for the system is obtained. Then, the sufficient criteria of robust asymptotical stability and stabilization for the addressed models are established, respectively. Finally, we give two numerical examples to verify the feasibility and effectiveness of the theoretical results.

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