IEEE Transactions on Neural Networks
On stability of recurrent neural networks-an approach from volterra integro-differential equations
IEEE Transactions on Neural Networks
Stability analysis for stochastic Cohen-Grossberg neural networks with mixed time delays
IEEE Transactions on Neural Networks
New Delay-Dependent Stability Criteria for Neural Networks With Time-Varying Delay
IEEE Transactions on Neural Networks
Stability and Hopf Bifurcation in a Simplified BAM Neural Network With Two Time Delays
IEEE Transactions on Neural Networks
Global Asymptotic Stability of Delayed Cellular Neural Networks
IEEE Transactions on Neural Networks
Discrete-Time Analogs for a Class of Continuous-Time Recurrent Neural Networks
IEEE Transactions on Neural Networks
Stability Analysis for Neural Networks With Time-Varying Interval Delay
IEEE Transactions on Neural Networks
A New Criterion of Delay-Dependent Asymptotic Stability for Hopfield Neural Networks With Time Delay
IEEE Transactions on Neural Networks
Further Results on Delay-Dependent Stability Criteria of Neural Networks With Time-Varying Delays
IEEE Transactions on Neural Networks
An augmented LKF approach involving derivative information of both state and delay
IEEE Transactions on Neural Networks
Delay-derivative-dependent stability for delayed neural networks with unbound distributed delay
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Journal of Computational and Applied Mathematics
New stability criteria for recurrent neural networks with a time-varying delay
International Journal of Automation and Computing
Robust H∞ filter design of delayed neural networks
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part I
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This brief deals with the problem of global asymptotic stability for a class of delayed neural networks. Some new Lyapunov-Krasovskii functionals are constructed by nonuniformly dividing the delay interval into multiple segments, and choosing proper functionals with different weighting matrices corresponding to different segments in the Lyapunov-Krasovskii functionals. Then using these new Lyapunov-Krasovskii functionals, some new delay-dependent criteria for global asymptotic stability are derived for delayed neural networks, where both constant time delays and time-varying delays are treated. These criteria are much less conservative than some existing results, which is shown through a numerical example.