Stability Analysis for Stochastic BAM Neural Networks with Distributed Time Delays

2010 ◽  
pp. 727-734
Author(s):  
Guanjun Wang
Keyword(s):  
Neural Networks ◽  
Stability Analysis ◽  
Time Delays ◽  
Bam Neural Networks ◽  
Distributed 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

Synchronization stability on the BAM neural networks with mixed time delays

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Ahmadjan Muhammadhaji ◽  
Zhidong Teng
Keyword(s):  
Neural Networks ◽  
Time Delays ◽  
Lyapunov Method ◽  
Sufficient Conditions ◽  
Time Varying ◽  
Bam Neural Networks ◽  
Bidirectional Associative Memory ◽  
Mixed Time Delays ◽  
Distributed Time Delays ◽  
Inequality Techniques

AbstractThis article investigates the general decay synchronization (GDS) for the bidirectional associative memory neural networks (BAMNNs). Compared with previous research results, both time-varying delays and distributed time delays are taken into consideration. By using Lyapunov method and using useful inequality techniques, some sufficient conditions on the GDS for BAMNNs are derived. Finally, a numerical example is also carried out to validate the practicability and feasibility of our proposed results. It is worth pointing out that the GDS may be specialized as exponential synchronization, polynomial synchronization and logarithmic synchronization. Besides, we can estimate the convergence rate of the synchronization by GDS. The obtained results in this article can be seen as the improvement and extension of the previously known works.

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