Journal of Computational and Applied Mathematics
Robust Stability of Switched Cohen–Grossberg Neural Networks With Mixed Time-Varying Delays
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Robust global exponential stability of Cohen-Grossberg neural networks with time delays
IEEE Transactions on Neural Networks
Stability analysis for stochastic Cohen-Grossberg neural networks with mixed time delays
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Further Stability Analysis for Neural Networks with Time-Varying Interval Delay
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
ISICA '09 Proceedings of the 4th International Symposium on Advances in Computation and Intelligence
Delay-derivative-dependent stability for delayed neural networks with unbound distributed delay
IEEE Transactions on Neural Networks
Stability analysis of generalised Neural Networks with mixed time-varying delays
International Journal of Systems, Control and Communications
Journal of Computational and Applied Mathematics
Mathematical and Computer Modelling: An International Journal
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In this paper, the global exponential stability is investigated for the Cohen-Grossberg neural networks with time-varying and distributed delays. By using a novel Lyapunov-Krasovskii functional and equivalent descriptor form of addressed system, the delay-dependent sufficient conditions are obtained to guarantee the exponential stability of the considered system. These conditions are expressed in terms of LMIs, and can be checked by resorting to the Matlab LMI toolbox. In addition, the proposed stability criteria do not require the monotonicity of the activation functions and the derivative of a time-varying delay being less than 1, which generalize and improve those earlier methods. Finally, numerical examples are given to show the reduced conservatism of the obtained methods.