Stability of stochastic neural networks
Neural, Parallel & Scientific Computations
Exponential stability of Cohen-Grossberg neural networks
Neural Networks
IEEE Transactions on Neural Networks
Impulsive Consensus for Complex Dynamical Networks with Nonidentical Nodes and Coupling Time-Delays
SIAM Journal on Control and Optimization
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IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Stability analysis for neural dynamics with time-varying delays
IEEE Transactions on Neural Networks
Stability analysis of bidirectional associative memory networks with time delays
IEEE Transactions on Neural Networks
Generalized function projective lag synchronization between two different neural networks
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
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This paper devotes to the stochastic robust stability of uncertain neural networks with time-varying delay and impulses. By using Lyapunov function and stochastic analysis approaches, a sufficient condition is derived in terms of linear matrix inequality (LMI), which can guarantee the uncertain neural network to be robustly exponentially stable in the mean square for all admissible uncertainties. We also extend the delay fractioning approach to the uncertainty system by constructing a Lyapunov-Krasovskii functional and comparing to a linear discrete system. The estimation of decay rate of uncertain neural network can be obtained by estimation of the decay of the linear discrete system. Meanwhile, two examples with numerical simulations are given to illustrate the applicability of the results.