Asymptotical Stability of Riemann–Liouville Fractional-Order Neutral-Type Delayed Projective Neural Networks

2019 ◽  
Vol 50 (1) ◽  
pp. 565-579 ◽  
Author(s):  
Jin-dong Li ◽  
Zeng-bao Wu ◽  
Nan-jing Huang
Keyword(s):  
Neural Networks ◽  
Fractional Order ◽  
Neutral Type ◽  
Asymptotical 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

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

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 Global Asymptotical Stability of Neutral-Type Neural Networks with D-Operator and Mixed Delays

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Qing Yang ◽  
Bo Du ◽  
Xiwang Cheng
Keyword(s):  
Neural Networks ◽  
Periodic Solutions ◽  
Neutral Type ◽  
Sufficient Conditions ◽  
Asymptotical Stability ◽  
Mixed Delays ◽  
Global Asymptotical Stability ◽  
Numerical Example ◽  
The Stability ◽  
Theoretical Results

In this manuscript, we investigate the stability problems of neutral-type neural networks with D-operator and mixed delays. Some sufficient conditions are obtained for guaranteeing the existence, uniqueness, and global asymptotical stability of periodic solutions to the considered neural networks. Finally, a numerical example is performed to illustrate the theoretical results.

scholarly journals Finite-Time Mittag–Leffler Synchronization of Neutral-Type Fractional-Order Neural Networks with Leakage Delay and Time-Varying Delays

Mathematics ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 1146
Author(s):  
Călin-Adrian Popa ◽  
Eva Kaslik
Keyword(s):  
Neural Networks ◽  
Feedback Control ◽  
Fractional Order ◽  
Finite Time ◽  
State Feedback ◽  
Neutral Type ◽  
Leakage Delay ◽  
State Feedback Control ◽  
Time Varying ◽  
Control Scheme

This paper studies fractional-order neural networks with neutral-type delay, leakage delay, and time-varying delays. A sufficient condition which ensures the finite-time synchronization of these networks based on a state feedback control scheme is deduced using the generalized Gronwall–Bellman inequality. Then, a different state feedback control scheme is employed to realize the finite-time Mittag–Leffler synchronization of these networks by using the fractional-order extension of the Lyapunov direct method for Mittag–Leffler stability. Two numerical examples illustrate the feasibility and the effectiveness of the deduced sufficient criteria.

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