Globally exponential synchronization and synchronizability for general dynamical networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Impulsive control and synchronization for delayed neural networks with reaction-diffusion terms
IEEE Transactions on Neural Networks
Computation of synchronized periodic solution in a BAM network with two delays
IEEE Transactions on Neural Networks
Exponential synchronization of hybrid coupled networks with delayed coupling
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Cluster synchronization for discrete-time complex networks
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part I
Quasi-synchronization of delayed coupled networks with non-identical discontinuous nodes
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
Stability analysis for impulsive coupled systems on networks
Neurocomputing
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This paper is concerned with the robust synchronization problem for an array of coupled stochastic discrete-time neural networks with time-varying delay. The individual neural network is subject to parameter uncertainty, stochastic disturbance, and time-varying delay, where the norm-bounded parameter uncertainties exist in both the state and weight matrices, the stochastic disturbance is in the form of a scalar Wiener process, and the time delay enters into the activation function. For the array of coupled neural networks, the constant coupling and delayed coupling are simultaneously considered. We aim to establish easy-to-verify conditions under which the addressed neural networks are synchronized. By using the Kronecker product as an effective tool, a linear matrix inequality (LMI) approach is developed to derive several sufficient criteria ensuring the coupled delayed neural networks to be globally, robustly, exponentially synchronized in the mean square. The LMI-based conditions obtained are dependent not only on the lower bound but also on the upper bound of the time-varying delay, and can be solved efficiently via the Matlab LMI Toolbox. Two numerical examples are given to demonstrate the usefulness of the proposed synchronization scheme.