Mean square exponential stability of stochastic fuzzy Hopfield neural networks with discrete and distributed time-varying delays

2009 ◽  
Vol 72 (7-9) ◽  
pp. 2017-2023 ◽  
Author(s):  
Hongyi Li ◽  
Bing Chen ◽  
Chong Lin ◽  
Qi Zhou
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Hopfield Neural Networks ◽  
Time Varying ◽  
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.

Mean-Square Exponential Stability of Stochastic Interval Cellular Neural Networks with Time-Varying Delay

2013 ◽  
Vol 760-762 ◽  
pp. 1742-1747
Author(s):  
Jin Fang Han
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Cellular Neural Networks ◽  
Time Varying ◽  
Mean Square ◽  
Razumikhin Theorem ◽  
Time Varying Delay ◽  
Mean Square Exponential Stability ◽  
The Mean ◽  
Varying Delay

This paper is concerned with the mean-square exponential stability analysis problem for a class of stochastic interval cellular neural networks with time-varying delay. By using the stochastic analysis approach, employing Lyapunov function and norm inequalities, several mean-square exponential stability criteria are established in terms of the formula and Razumikhin theorem to guarantee the stochastic interval delayed cellular neural networks to be mean-square exponential stable. Some recent results reported in the literatures are generalized. A kind of equivalent description for this stochastic interval cellular neural networks with time-varying delay is also given.

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