New results for global robust asymptotic stability of BAM neural networks with time-varying delays

2010 ◽  
Vol 74 (1-3) ◽  
pp. 337-342 ◽  
Author(s):  
Yufa Yuan ◽  
Xiaolin Li
Keyword(s):  
Neural Networks ◽  
Asymptotic Stability ◽  
Time Varying ◽  
Bam Neural Networks ◽  
Global Robust Asymptotic Stability ◽  
Robust Asymptotic Stability

DELAY-DEPENDENT GLOBAL ROBUST ASYMPTOTIC STABILITY ANALYSIS OF BAM NEURAL NETWORKS WITH TIME DELAY: AN LMI APPROACH

2007 ◽  
Vol 03 (01) ◽  
pp. 57-68 ◽  
Author(s):  
XU-YANG LOU ◽  
BAO-TONG CUI
Keyword(s):  
Neural Networks ◽  
Asymptotic Stability ◽  
Linear Matrix ◽  
Time Varying ◽  
Bam Neural Networks ◽  
Lmi Approach ◽  
Global Robust Asymptotic Stability ◽  
Dependent Property ◽  
Delay Dependent ◽  
Robust Asymptotic Stability

The global robust asymptotic stability of bi-directional associative memory (BAM) neural networks with constant or time-varying delays is studied. An approach combining the Lyapunov-Krasovskii functional with the linear matrix inequality (LMI) is taken to study the problem. Some a criteria for the global robust asymptotic stability, which gives information on the delay-dependent property, are derived. Some illustrative examples are given to demonstrate the effectiveness of the obtained results.

Sign in / Sign up

Export Citation Format

Share Document