Robust stability of Cohen-Grossberg neural networks via state transmission matrix
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
On exponential stability results for fuzzy impulsive neural networks
Fuzzy Sets and Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Robust stability of delayed fuzzy Cohen-Grossberg neural networks
Computers & Mathematics with Applications
Stability of neural networks with both impulses and time-varying delays on time scale
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part I
Expert Systems with Applications: An International Journal
Mathematical and Computer Modelling: An International Journal
ACM Transactions on Sensor Networks (TOSN)
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In this correspondence, the impulsive effects on the stability of fuzzy Cohen-Grossberg neural networks (FCGNNs) with time-varying delays are considered. Several sufficient conditions are obtained ensuring global exponential stability of equilibrium point for the neural networks by the idea of vector Lyapunov function, M-matrix theory, and analytic methods. Moreover, the estimation for exponential convergence rate index is proposed. The obtained results not only show that the stability still remains under certain impulsive perturbations for the continuous stable FCGNNs with time-varying delays, but also present an approach to stabilize the unstable FCGNNs with time-varying delays by utilizing impulsive effects. An example with simulations is given to show the effectiveness of the obtained results