ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
Dynamic Analysis of Delayed Fuzzy Cellular Neural Networks with Time-Varying Coefficients
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
IEEE Transactions on Circuits and Systems II: Express Briefs
Delay-dependent globally exponential stability criteria for static neural networks: an LMI approach
IEEE Transactions on Circuits and Systems II: Express Briefs
IEEE Transactions on Circuits and Systems Part I: Regular Papers
Adaptive statistic tracking control based on two-step neural networks with time delays
IEEE Transactions on Neural Networks
Segmented-memory recurrent neural networks
IEEE Transactions on Neural Networks
Nonlinear time series online prediction using reservoir Kalman filter
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Improved robust stability criteria for delayed cellular neural networks via the LMI approach
IEEE Transactions on Circuits and Systems II: Express Briefs
Novel weighting-delay-based stability criteria for recurrent neural networks with time-varying delay
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 Neural Networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Neural Networks
Universal analysis method for stability of recurrent neural networks with different multiple delays
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part I
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
Novel stability criteria of Cohen–Grossberg neural networks with time-varying delays
International Journal of Circuit Theory and Applications
International Journal of Innovative Computing and Applications
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In this paper, several sufficient conditions are established for the global asymptotic stability of recurrent neural networks with multiple time-varying delays. The Lyapunov-Krasovskii stability theory for functional differential equations and the linear matrix inequality (LMI) approach are employed in our investigation. The results are shown to be generalizations of some previously published results and are less conservative than existing results. The present results are also applied to recurrent neural networks with constant time delays.