Stability analysis of delayed cellular neural networks
Neural Networks
Global attractivity in delayed Hopfield neural network models
SIAM Journal on Applied Mathematics
On impulsive autoassociative neural networks
Neural Networks
Exponential stability of continuous-time and discrete-time cellular neural networks with delays
Applied Mathematics and Computation
Stability analysis of dynamical neural networks
IEEE Transactions on Neural Networks
Stability analysis of Hopfield-type neural networks
IEEE Transactions on Neural Networks
On equilibria, stability, and instability of Hopfield neural networks
IEEE Transactions on Neural Networks
Stability of asymmetric Hopfield networks
IEEE Transactions on Neural Networks
Exponential stability and periodic oscillatory solution in BAM networks with delays
IEEE Transactions on Neural Networks
Stability analysis for stochastic Cohen-Grossberg neural networks with mixed time delays
IEEE Transactions on Neural Networks
Global exponential stability of impulsive neural networks with variable delay: an LMI approach
IEEE Transactions on Circuits and Systems Part I: Regular Papers
Globally exponential stability of a class of impulsive neural networks with variable delays
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Globally Exponential Stability for Delayed Neural Networks Under Impulsive Control
Neural Processing Letters
IEEE Transactions on Neural Networks
Stability of impulsive cohen-grossberg neural networks with delays
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
Globally exponential stability of a class of neural networks with impulses and variable delays
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
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A class of delayed cellular neural networks with impulses (DCNN) is investigated in this paper. Sufficient conditions are obtained for the existence of unique and globally exponential stable equilibriums of the DCNNs with Lipschitzian activation functions without assuming their boundedness, monotonicity or differentiability, but subjected to impulsive state displacement at fixed instants of time. The sufficient conditions are easy to verify and when the impulsive jumps are absent, the results reduce to those of the non-impulsive systems. Our investigations are based on employing Banach's fixed point theorem, matrix and associated spectral theory. Our results generalize and significantly improve the previous known results due to this method. An example is given to show their feasibility and effectiveness.