inertial neural networks
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2022 ◽  
Vol 355 ◽  
pp. 02006
Author(s):  
Adnène Arbi ◽  
Najeh Tahri

In this work, since the importance of investigation of oscillators solutions, an methodology for proving the existence and stability of almost anti-periodic solutions of inertial neural networks model on time scales are discussed. By developing an approach based on differential inequality techniques coupled with Lyapunov function method. A numerical example is given for illustration.


2022 ◽  
Vol 27 (1) ◽  
pp. 1-18
Author(s):  
Chaouki Aouiti ◽  
Jinde Cao ◽  
Hediene Jallouli ◽  
Chuangxia Huang

This paper deals with the finite-time stabilization of fractional-order inertial neural network with varying time-delays (FOINNs). Firstly, by correctly selected variable substitution, the system is transformed into a first-order fractional differential equation. Secondly, by building Lyapunov functionalities and using analytical techniques, as well as new control algorithms (which include the delay-dependent and delay-free controller), novel and effective criteria are established to attain the finite-time stabilization of the addressed system. Finally, two examples are used to illustrate the effectiveness and feasibility of the obtained results.


Symmetry ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2231
Author(s):  
Jian Zhang ◽  
Ancheng Chang ◽  
Gang Yang

The classical Hopefield neural networks have obvious symmetry, thus the study related to its dynamic behaviors has been widely concerned. This research article is involved with the neutral-type inertial neural networks incorporating multiple delays. By making an appropriate Lyapunov functional, one novel sufficient stability criterion for the existence and global exponential stability of T-periodic solutions on the proposed system is obtained. In addition, an instructive numerical example is arranged to support the present approach. The obtained results broaden the application range of neutral-types inertial neural networks.


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