Robust exponential stability analysis for interval Cohen–Grossberg type BAM neural networks with mixed time delays

2013 ◽  
Vol 5 (1) ◽  
pp. 23-38 ◽  
Author(s):  
Qingqing He ◽  
Deyou Liu ◽  
Huaiqin Wu ◽  
Sanbo Ding
Keyword(s):  
Neural Networks ◽  
Stability Analysis ◽  
Exponential Stability ◽  
Time Delays ◽  
Bam Neural Networks ◽  
Robust Exponential Stability ◽  
Mixed Time Delays

Impulsive Cohen–Grossberg BAM neural networks with mixed time-delays: An exponential stability analysis issue

2018 ◽  
Vol 275 ◽  
pp. 2588-2602 ◽  
Author(s):  
C. Maharajan ◽  
R. Raja ◽  
Jinde Cao ◽  
G. Rajchakit ◽  
Ahmed Alsaedi
Keyword(s):  
Neural Networks ◽  
Stability Analysis ◽  
Exponential Stability ◽  
Time Delays ◽  
Bam Neural Networks ◽  
Mixed Time Delays

Exponential Convergence Rate Estimation for a Class of BAM Neural Networks with Time-Delays

2010 ◽  
Vol 143-144 ◽  
pp. 707-711
Author(s):  
Jian Dong Yu
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Decay Rate ◽  
Time Delays ◽  
Linear Matrix ◽  
Bam Neural Networks ◽  
Parameter Uncertainties ◽  
Mixed Time Delays ◽  
Weighting Matrix ◽  
Rate Dependent

This paper is concerned with the exponential stability analysis problem for a class of neutral bidirectional associative memory (BAM) neural networks with parameter uncertainties and mixed time-delays where the parameter uncertainties are norm-bounded and the mixed time-delays involve discrete, distributed and neutral time-delays. By utilizing free-weighting matrix method and an appropriately constructed Lyapunov-Krasovskii Functional, some nove delay-dependent and decay-rate dependent exponential stability criteria are derived in the terms of linear matrix inequalities (LMIs). Meanwhile, the maximum allowable decay rate can be estimated based on the obtained results. Two numerical examples are presented to demonstrate the effectiveness of the proposed method.

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