Robust mean square stability of delayed stochastic generalized uncertain impulsive reaction-diffusion neural networks

2021 ◽  
Vol 358 (1) ◽  
pp. 877-894 ◽  
Author(s):  
Suriguga Mongolian ◽  
Yonggui Kao ◽  
Changhong Wang ◽  
Hongwei Xia
Keyword(s):  
Neural Networks ◽  
Reaction Diffusion ◽  
Mean Square ◽  
Mean Square Stability ◽  
Impulsive Reaction

scholarly journals Mean-square stability of Riemann–Liouville fractional Hopfield’s graded response neural networks with random impulses

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
R. Agarwal ◽  
S. Hristova ◽  
D. O’Regan ◽  
P. Kopanov
Keyword(s):  
Neural Networks ◽  
Fractional Derivative ◽  
Equilibrium Point ◽  
Time Varying ◽  
Mean Square ◽  
Mean Square Stability ◽  
Graded Response ◽  
Liouville Fractional Derivative ◽  
Set Up ◽  
Type Of Stability

AbstractIn this paper a model of Hopfield’s graded response neural network is investigated. A network whose neurons are subject to a certain impulsive state displacement at random times is considered. The model is set up and studied. The presence of random moments of impulses in the model leads to a change of the solutions to stochastic processes. Also, we use the Riemann–Liouville fractional derivative to model adequately the long-term memory and the nonlocality in the neural networks. We set up in an appropriate way both the initial conditions and the impulsive conditions at random moments. The application of the Riemann–Liouville fractional derivative leads to a new definition of the equilibrium point. We define mean-square Mittag-Leffler stability in time of the equilibrium point of the model and study this type of stability. Some sufficient conditions for this type of stability are obtained. The general case with time varying self-regulating parameters of all units and time varying functions of the connection between two neurons is studied.

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