STABILITY OF DEAD ZONE BIDIRECTIONAL ASSOCIATIVE MEMORY NEURAL NETWORKS INVOLVING TIME DELAYS

2002 ◽  
Vol 12 (01) ◽  
pp. 15-29 ◽  
Author(s):  
V. SREE HARI RAO ◽  
P. RAJA SEKHARA RAO
Keyword(s):  
Associative Memory ◽  
Time Delays ◽  
Dead Zone ◽  
Sufficient Conditions ◽  
System Of Equations ◽  
Bidirectional Associative Memory ◽  
Transmission Delays ◽  
Asymptotic Nature ◽  
The Given ◽  
Equilibrium Pattern

A mathematical model describing the dynamical interactions of bidirectional associative memory networks involving transmission delays is considered. The influence of a dead zone or a zone of noactivation on the global stability is investigated and various easily verifiable sets of sufficient conditions are established. The asymptotic nature of solutions when the given system of equations does not possess an equilibrium pattern is discussed.

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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