Further mean-square asymptotic stability of impulsive discrete-time stochastic BAM neural networks with Markovian jumping and multiple time-varying delays

2019 ◽  
Vol 356 (1) ◽  
pp. 561-591 ◽  
Author(s):  
C. Sowmiya ◽  
R. Raja ◽  
Quanxin Zhu ◽  
G. Rajchakit
Keyword(s):  
Neural Networks ◽  
Asymptotic Stability ◽  
Discrete Time ◽  
Multiple Time ◽  
Time Varying ◽  
Mean Square ◽  
Bam Neural Networks ◽  
Markovian Jumping

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.

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