Mean square exponential stability of stochastic delayed Hopfield neural networks

2005 ◽  
Vol 343 (4) ◽  
pp. 306-318 ◽  
Author(s):  
Li Wan ◽  
Jianhua Sun
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Hopfield Neural Networks ◽  
Mean Square ◽  
Mean Square Exponential Stability

Mean Square Exponential Stability of Stochastic High-Order Hopfield Neural Networks with Time-Varying Delays

2013 ◽  
Vol 303-306 ◽  
pp. 1532-1535
Author(s):  
Xiang Dong Shi
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Neural System ◽  
High Order ◽  
Hopfield Neural Networks ◽  
Linear Matrix ◽  
Time Varying ◽  
Mean Square ◽  
Neuron Activation ◽  
Mean Square Exponential Stability

The paper considers the problems of global exponential stability for stochastic delayed high-order Hopfield neural networks with time-varying delays. By employing the linear matrix inequality(LMI) and the Lyapunov functional methods, we present some new criteria ensuring globally mean square exponential stability. The results impose constraint conditions on the network parameters of neural system independent. The results are applicable to all continuous non-monotonic neuron activation functions.

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