Delay-dependent stability analysis for continuous-time BAM neural networks with Markovian jumping parameters

2010 ◽  
Vol 23 (3) ◽  
pp. 315-321 ◽  
Author(s):  
Hongyang Liu ◽  
Yan Ou ◽  
Jun Hu ◽  
Tingting Liu
Keyword(s):  
Neural Networks ◽  
Stability Analysis ◽  
Continuous Time ◽  
Bam Neural Networks ◽  
Markovian Jumping Parameters ◽  
Markovian Jumping ◽  
Delay Dependent ◽  
Delay Dependent Stability

Robust Exponential Stability of Stochastic Discrete-Time BAM Neural Networks with Markovian Jumping Parameters and Delays

2014 ◽  
Vol 989-994 ◽  
pp. 1877-1882 ◽  
Author(s):  
Liang Dong Guo ◽  
Jin Nie ◽  
You Shan Zhang
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Discrete Time ◽  
Linear Matrix ◽  
Bam Neural Networks ◽  
Markovian Jumping Parameters ◽  
Markovian Jumping ◽  
Robust Exponential Stability ◽  
Globally Exponential Stability ◽  
Delay Dependent

The problem of robustly globally exponential stability in the mean square is investigated for stochastic uncertain discrete-time bidirectional associative memory (BAM) neural networks with time-varying delays and Markovian jumping parameters. The uncertainties are assumed to be the linear fractional form. By using Lyapunov-Krasovskii functional (LKF) method and some novel technique, a delay-dependent exponential stability criterion is established in terms of linear matrix inequalities (LMIs). A numerical example is provided to show the effectiveness and the improvement of the proposed methods.

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