A new Lyapunov functional for stability analysis of neutral-type Hopfield neural networks with multiple delays

2020 ◽  
Vol 129 ◽  
pp. 288-297 ◽  
Author(s):  
Ozlem Faydasicok
Keyword(s):  
Neural Networks ◽  
Stability Analysis ◽  
Lyapunov Functional ◽  
Neutral Type ◽  
Hopfield Neural Networks ◽  
Multiple Delays

scholarly journals Periodicity on Neutral-Type Inertial Neural Networks Incorporating Multiple Delays

Symmetry ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2231
Author(s):  
Jian Zhang ◽  
Ancheng Chang ◽  
Gang Yang
Keyword(s):  
Neural Networks ◽  
Stability Criterion ◽  
Lyapunov Functional ◽  
Global Exponential Stability ◽  
Neutral Type ◽  
Multiple Delays ◽  
Application Range ◽  
Research Article ◽  
Sufficient Stability ◽  
Inertial Neural Networks

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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