Discrete-time bidirectional associative memory neural networks with variable delays

2005 ◽  
Vol 335 (2-3) ◽  
pp. 226-234 ◽  
Author(s):  
Jinling Liang ◽  
Jinde Cao ◽  
Daniel W.C. Ho
Keyword(s):  
Neural Networks ◽  
Associative Memory ◽  
Discrete Time ◽  
Bidirectional Associative Memory ◽  
Variable Delays

scholarly journals Global Exponential Stability of Discrete-Time Multidirectional Associative Memory Neural Network with Variable Delays

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Min Wang ◽  
Tiejun Zhou ◽  
Xiaolan Zhang
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Equilibrium Point ◽  
Associative Memory ◽  
Discrete Time ◽  
Global Exponential Stability ◽  
Sufficient Condition ◽  
Variable Delays ◽  
Exponentially Stable ◽  
Inequality Technique

A discrete-time multidirectional associative memory neural networks model with varying time delays is formulated by employing the semidiscretization method. A sufficient condition for the existence of an equilibrium point is given. By calculating difference and using inequality technique, a sufficient condition for the global exponential stability of the equilibrium point is obtained. The results are helpful to design global exponentially stable multidirectional associative memory neural networks. An example is given to illustrate the effectiveness of the results.

Robust stability analysis for delayed Cohen-Grossberg-type bidirectional associative memory neural networks with norm-bounded uncertainties

2009 ◽  
Vol 223 (5) ◽  
pp. 693-707
Author(s):  
Y Wang ◽  
P Hu
Keyword(s):  
Neural Networks ◽  
Associative Memory ◽  
Robust Stability ◽  
Sufficient Conditions ◽  
Linear Matrix ◽  
Bidirectional Associative Memory ◽  
Parameter Uncertainties ◽  
Robust Stability Analysis ◽  
Global Robust Stability ◽  
Bounded Uncertainties

In this paper, the problem of global robust stability is discussed for uncertain Cohen-Grossberg-type (CG-type) bidirectional associative memory (BAM) neural networks (NNs) with delays. The parameter uncertainties are supposed to be norm bounded. The sufficient conditions for global robust stability are derived by employing a Lyapunov-Krasovskii functional. Based on these, the conditions ensuring global asymptotic stability without parameter uncertainties are established. All conditions are expressed in terms of linear matrix inequalities (LMIs). In addition, two examples are provided to illustrate the effectiveness of the results obtained.

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