Multiple Lagrange stability and Lyapunov asymptotical stability of delayed fractional-order Cohen–Grossberg neural networks

2020 ◽  
Vol 29 (2) ◽  
pp. 020703
Author(s):  
Yu-Jiao Huang ◽  
Xiao-Yan Yuan ◽  
Xu-Hua Yang ◽  
Hai-Xia Long ◽  
Jie Xiao
Keyword(s):  
Neural Networks ◽  
Fractional Order ◽  
Asymptotical Stability ◽  
Lagrange Stability

Multiple asymptotical stability analysis for fractional-order neural networks with time delays

2019 ◽  
Vol 50 (10) ◽  
pp. 2063-2076 ◽  
Author(s):  
Liguang Wan ◽  
Xisheng Zhan ◽  
Hongliang Gao ◽  
Qingsheng Yang ◽  
Tao Han ◽  
...  
Keyword(s):  
Neural Networks ◽  
Stability Analysis ◽  
Fractional Order ◽  
Time Delays ◽  
Asymptotical Stability

scholarly journals Global Asymptotical Stability Analysis for Fractional Neural Networks with Time-Varying Delays

Mathematics ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 138
Author(s):  
Zhixin Zhang ◽  
Yufeng Zhang ◽  
Jia-Bao Liu ◽  
Jiang Wei
Keyword(s):  
Neural Networks ◽  
Fractional Order ◽  
Asymptotical Stability ◽  
Time Varying ◽  
Global Asymptotical Stability ◽  
Stability Criteria ◽  
Lmi Approach ◽  
Time Varying Delay ◽  
The Stability ◽  
Varying Delay

In this paper, the global asymptotical stability of Riemann-Liouville fractional-order neural networks with time-varying delays is studied. By combining the Lyapunov functional function and LMI approach, some sufficient criteria that guarantee the global asymptotical stability of such fractional-order neural networks with both discrete time-varying delay and distributed time-varying delay are derived. The stability criteria is suitable for application and easy to be verified by software. Lastly, some numerical examples are presented to check the validity of the obtained results.

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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