Stability analysis of delayed cellular neural networks
Neural Networks
Convergence of Delayed Dynamical Systems
Neural Processing Letters
Global exponential stability of delayed Hopfield neural networks
Neural Networks
Exponential Convergence of Delayed Dynamical Systems
Neural Computation
Global stability for cellular neural networks with time delay
IEEE Transactions on Neural Networks
Stability of asymmetric Hopfield networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Global exponential stability in DCNNs with distributed delays and unbounded activations
Journal of Computational and Applied Mathematics
Journal of Computational and Applied Mathematics
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Novel LMI Criteria for Stability of Neural Networks with Distributed Delays
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Journal of Computational and Applied Mathematics
Global Passivity of Stochastic Neural Networks with Time-Varying Delays
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
Passivity analysis of neural networks with discrete and distributed delays
International Journal of Systems, Control and Communications
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - 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
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In this paper, we have derived some sufficient conditions for existence and uniqueness of equilibrium and global exponential stability in delayed Hopfield neural networks by using a different approach from the usually used one where the existence, uniqueness of equilibrium and stability are proved in two separate steps, rather we first prove global exponential convergence to 0 of the difference between any two solutions of the original neural networks, the existence and uniqueness of equilibrium is the direct results of this procedure. We obtain the conditions by suitable construction of Lyapunov functionals and estimation of derivates of the Lyapunov functionals by the well-known Young's inequality and Holder's inequality. The proposed conditions are related to p-norms of vector or matrix, p ∈ [1, ∞], and thus unify and generalize some results in the literature.