Global Exponential Convergence of Neutral Type Competitive Neural Networks with D Operator and Mixed Delay

2020 ◽  
Vol 33 (6) ◽  
pp. 1785-1803
Author(s):  
Chaouki Aouiti ◽  
El Abed Assali ◽  
Imen Ben Gharbia
Keyword(s):  
Neural Networks ◽  
Exponential Convergence ◽  
Neutral Type ◽  
D Operator ◽  
Global Exponential Convergence ◽  
Mixed Delay

Convergence of neutral type proportional-delayed HCNNs with D operators

2019 ◽  
Vol 12 (01) ◽  
pp. 1950002 ◽  
Author(s):  
Yanli Xu ◽  
Jiaming Zhong
Keyword(s):  
Neural Networks ◽  
Numerical Simulations ◽  
Differential Inequality ◽  
Exponential Convergence ◽  
Neutral Type ◽  
Cellular Neural Networks ◽  
High Order ◽  
Proportional Delays ◽  
Global Exponential Convergence ◽  
Inequality Techniques

This paper is concerned with neutral type high-order cellular neural networks (HCNNs) involving proportional delays and [Formula: see text] operators. Some criteria are established for the global exponential convergence of the addressed models by using differential inequality techniques. Moreover, an example and its numerical simulations are employed to illustrate the main results.

Exponential convergence rate estimation for uncertain delayed neural networks of neutral type

2009 ◽  
Vol 40 (5) ◽  
pp. 2491-2499 ◽  
Author(s):  
Chang-Hua Lien ◽  
Ker-Wei Yu ◽  
Yen-Feng Lin ◽  
Yeong-Jay Chung ◽  
Long-Yeu Chung
Keyword(s):  
Neural Networks ◽  
Convergence Rate ◽  
Exponential Convergence ◽  
Neutral Type ◽  
Delayed Neural Networks ◽  
Rate Estimation ◽  
Exponential Convergence Rate
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