Exponential stability of stochastic high-order BAM neural networks with time delays and impulsive effects

2012 ◽  
Vol 23 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Danjie Lu ◽  
Chaojie Li
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Time Delays ◽  
High Order ◽  
Impulsive Effects ◽  
Bam Neural Networks

Impulsive effects on stability of high-order BAM neural networks with time delays

2011 ◽  
Vol 74 (10) ◽  
pp. 1541-1550 ◽  
Author(s):  
Chaojie Li ◽  
Chuandong Li ◽  
Xiaofeng Liao ◽  
Tingwen Huang
Keyword(s):  
Neural Networks ◽  
Time Delays ◽  
High Order ◽  
Impulsive Effects ◽  
Bam Neural Networks

GLOBAL EXPONENTIAL STABILITY OF HIGH-ORDER BAM NEURAL NETWORKS WITH REACTION–DIFFUSION TERMS

2010 ◽  
Vol 20 (10) ◽  
pp. 3209-3223 ◽  
Author(s):  
FENG-YAN ZHOU ◽  
CHENG-RONG MA
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Time Delays ◽  
Global Exponential Stability ◽  
Sufficient Conditions ◽  
Reaction Diffusion ◽  
Mean Value ◽  
High Order ◽  
Mean Value Theorem ◽  
Bam Neural Networks

The global exponential stability is studied for a class of high-order bi-directional associative memory (BAM) neural networks with time delays and reaction–diffusion terms. By constructing suitable Lyapunov functional, using differential mean value theorem and homeomorphism, several sufficient conditions guaranteeing the existence, uniqueness and global exponential stability of high-order BAM neural networks with time delays and reaction–diffusion terms are given. Two illustrative examples are also given in the end to show the effectiveness of our results.

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