ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
Delay-dependent globally exponential stability criteria for static neural networks: an LMI approach
IEEE Transactions on Circuits and Systems II: Express Briefs
Improved robust stability criteria for delayed cellular neural networks via the LMI approach
IEEE Transactions on Circuits and Systems II: Express Briefs
IEEE Transactions on Neural Networks
An augmented LKF approach involving derivative information of both state and delay
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
International Journal of Innovative Computing and Applications
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In this paper, robust stability problems for interval Cohen-Grossberg neural networks with unknown time-varying delays are investigated. Using linear matrix inequality, M -matrix theory, and Halanay inequality techniques, new sufficient conditions independent of time-varying delays are derived to guarantee the uniqueness and the global robust stability of the equilibrium point of interval Cohen-Grossberg neural networks with time-varying delays. All these results have no restriction on the rate of change of the time-varying delays. Compared to some existing results, these new criteria are less conservative and are more convenient to check. Two numerical examples are used to show the effectiveness of the present results.