An improved Lyapunov functional with application to stability of Cohen–Grossberg neural networks of neutral-type with multiple delays

2020 ◽  
Vol 132 ◽  
pp. 532-539
Author(s):  
Ozlem Faydasicok
Keyword(s):  
Neural Networks ◽  
Lyapunov Functional ◽  
Neutral Type ◽  
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.

EXPONENTIAL STABILITY FOR STOCHASTIC NEURAL NETWORKS OF NEUTRAL TYPE WITH IMPULSIVE EFFECTS

2010 ◽  
Vol 24 (11) ◽  
pp. 1099-1110 ◽  
Author(s):  
RATHINASAMY SAKTHIVEL ◽  
R. SAMIDURAI ◽  
S. MARSHAL ANTHONI
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Exponential Stability ◽  
Stochastic Analysis ◽  
Lyapunov Functional ◽  
Neutral Type ◽  
Impulsive Effects ◽  
Linear Matrix ◽  
Stochastic Neural Networks ◽  
Matrix Inequality

This paper is concerned with the exponential stability of stochastic neural networks of neutral type with impulsive effects. By employing the Lyapunov functional and stochastic analysis, a new stability criterion for the stochastic neural network is derived in terms of linear matrix inequality. A numerical example is provided to show the effectiveness and applicability of the obtained result.

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