Global Exponential Stability of High-Order Bidirectional Associative Memory (BAM) Neural Networks with Proportional Delays

2020 ◽  
Vol 51 (3) ◽  
pp. 2531-2549 ◽  
Author(s):  
Jiacheng Zu ◽  
Zhixian Yu ◽  
Yanling Meng
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Associative Memory ◽  
Global Exponential Stability ◽  
High Order ◽  
Bam Neural Networks ◽  
Bidirectional Associative Memory ◽  
Proportional Delays

DESTABILIZING EFFECTS OF IMPULSE IN DELAYED BAM NEURAL NETWORKS

2009 ◽  
Vol 23 (29) ◽  
pp. 3503-3513 ◽  
Author(s):  
CHUANDONG LI ◽  
CHAOJIE LI ◽  
CHAO LIU
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Equilibrium Point ◽  
Associative Memory ◽  
Global Exponential Stability ◽  
Dynamical Property ◽  
Bam Neural Networks ◽  
Bidirectional Associative Memory

This paper further studies the global exponential stability of the equilibrium point of the delayed bidirectional associative memory (DBAM) neural networks with impulse effects. Several results characterizing the aggregated effects of impulse and dynamical property of the impulse-free DBAM on the exponential stability of the considered DBAM have been established. It is shown that the impulsive DBAM will preserve the global exponential stability of the impulse-free DBAM even if the impulses have enlarging effects on the states of neurons.

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