Global exponential stability analysis of bidirectional associative memory neural networks with time-varying delays

2009 ◽  
Vol 10 (3) ◽  
pp. 1527-1539 ◽  
Author(s):  
Yangling Wang
Keyword(s):  
Neural Networks ◽  
Stability Analysis ◽  
Exponential Stability ◽  
Associative Memory ◽  
Global Exponential Stability ◽  
Time Varying ◽  
Bidirectional Associative Memory

scholarly journals Global Exponential Stability Criteria for Bidirectional Associative Memory Neural Networks with Time-Varying Delays

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
J. Thipcha ◽  
P. Niamsup
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Associative Memory ◽  
Global Exponential Stability ◽  
Linear Matrix ◽  
Time Varying ◽  
Stability Criteria ◽  
Bidirectional Associative Memory ◽  
Lower And Upper Bounds ◽  
Delay Dependent

The global exponential stability for bidirectional associative memory neural networks with time-varying delays is studied. In our study, the lower and upper bounds of the activation functions are allowed to be either positive, negative, or zero. By constructing new and improved Lyapunov-Krasovskii functional and introducing free-weighting matrices, a new and improved delay-dependent exponential stability for BAM neural networks with time-varying delays is derived in the form of linear matrix inequality (LMI). Numerical examples are given to demonstrate that the derived condition is less conservative than some existing results given in the literature.

scholarly journals Robust Stability Analysis of Neutral-Type Hybrid Bidirectional Associative Memory Neural Networks with Time-Varying Delays

2014 ◽  
Vol 2014 ◽  
pp. 1-12
Author(s):  
Wei Feng ◽  
Simon X. Yang ◽  
Haixia Wu
Keyword(s):  
Neural Networks ◽  
Stability Analysis ◽  
Associative Memory ◽  
Robust Stability ◽  
Neutral Type ◽  
Time Varying ◽  
Bidirectional Associative Memory ◽  
Numerical Examples ◽  
Network Parameters ◽  
Robust Stability Analysis

The global asymptotic robust stability of equilibrium is considered for neutral-type hybrid bidirectional associative memory neural networks with time-varying delays and parameters uncertainties. The results we obtained in this paper are delay-derivative-dependent and establish various relationships between the network parameters only. Therefore, the results of this paper are applicable to a larger class of neural networks and can be easily verified when compared with the previously reported literature results. Two numerical examples are illustrated to verify our results.

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