Stability analysis of delayed cellular neural networks
Neural Networks
Global attractivity in delayed Hopfield neural network models
SIAM Journal on Applied Mathematics
Global exponential stability and periodic solutions of delayed cellular neural networks
Journal of Computer and System Sciences
On the stability analysis of delayed neural networks systems
Neural Networks
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
Journal of Computational and Applied Mathematics
Journal of Computational and Applied Mathematics
Stability of non-autonomous delayed cellular neural networks
CIS'04 Proceedings of the First international conference on Computational and Information Science
Stability of nonautonomous recurrent neural networks with time-varying delays
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Robust stability analysis of a class of hopfield neural networks with multiple delays
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Global exponential stability of non-autonomous delayed neural networks
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Globally exponential stability of non-autonomous delayed neural networks
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Universal approach to study delayed dynamical systems
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
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In this paper, by constructing a new Lyapunov functional, and using M-matrix and topological degree tool, problem of the global asymptotic stability (GAS) is discussed for a class of recurrent neural networks with time-varying delays. Some simple and new sufficient conditions are obtained ensuring existence, uniqueness of the equilibrium point and its GAS of the neural networks. Some previous works are improved. In addition, this condition does not require the activation functions to be differentiable, bounded and monotone nondecreasing and the weight-connected matrices to be symmetric. The neural network model considered in this paper include the delayed Hopfield neural networks, bidirectional associative memory networks and delayed cellular neural networks as its special cases.