On the stability analysis of delayed neural networks systems
Neural Networks
Globally exponential stability conditions for cellular neural networks with time-varying delays
Applied Mathematics and Computation
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Stability analysis for neural dynamics with time-varying delays
IEEE Transactions on Neural Networks
Global stability for cellular neural networks with time delay
IEEE Transactions on Neural Networks
Exponential stability and periodic oscillatory solution in BAM networks with delays
IEEE Transactions on Neural Networks
Delay-independent stability in bidirectional associative memory networks
IEEE Transactions on Neural Networks
Stabilisation of Cellular Neural Networks with time-varying delays and reaction-diffusion terms
International Journal of Intelligent Systems Technologies and Applications
Global stability of a class of Cohen-Grossberg neural networks with delays
International Journal of Intelligent Systems Technologies and Applications
IEEE Transactions on Circuits and Systems II: Express Briefs
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In this paper, we essentially drop the requirement of Lipschitz condition on the activation functions. Only using physical parameters of neural networks, some new criteria concerning global exponential stability for a class of generalized neural networks with time-varying delays are obtained. The neural network model considered includes the delayed Hopfield neural networks, bidirectional associative memory networks, and delayed cellular neural networks as its special cases. Since these new criteria do not require the activation functions to be differentiable, bounded or monotone nondecreasing, the connection weight matrices to be symmetric and the delay function @t"i"j(t) to be differentiable, our results are mild and more general than previously known criteria. Four illustrative examples are given to demonstrate the effectiveness of the obtained results.