On delayed impulsive Hopfield neural networks
Neural Networks
On impulsive autoassociative neural networks
Neural Networks
Impulsive effects on stability of Cohen-Grossberg neural networks with variable delays
Applied Mathematics and Computation
Journal of Computational and Applied Mathematics
Computers & Mathematics with Applications
Neural Processing Letters
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
IEEE Transactions on Neural Networks
Impulsive control and synchronization for delayed neural networks with reaction-diffusion terms
IEEE Transactions on Neural Networks
Global exponential robust stability of hopfield neural networks with reaction-diffusion terms
ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and simulation and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part II
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
WSEAS Transactions on Mathematics
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
Mathematical and Computer Modelling: An International Journal
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In this paper, we investigate a class of impulsive Cohen-Grossberg neural networks with time-varying delays and reaction-diffusion terms. By establishing a delay differential inequality with impulsive initial conditions and employing M-matrix theory, we find some sufficient conditions ensuring the existence, uniqueness and global exponential stability of equilibrium point for impulsive Cohen-Grossberg neural networks with time-varying delays and reaction-diffusion terms. In particular, the estimate of the exponential convergence rate is also provided, which depends on the system parameters and delays. Two examples are given to illustrate the results obtained here.