Global attractivity in delayed Hopfield neural network models
SIAM Journal on Applied Mathematics
Qualitative Analysis and Synthesis of Recurrent Neural Networks
Qualitative Analysis and Synthesis of Recurrent Neural Networks
Global exponential stability of delayed Hopfield neural networks
Neural Networks
Robust stability for interval Hopfield neural networks with time delay
IEEE Transactions on Neural Networks
Estimate of exponential convergence rate and exponential stability for neural networks
IEEE Transactions on Neural Networks
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Without assuming the boundedness, strict monotonicity and differentiability of the activation function, a result is established for the global exponential stability of a class of neural networks with multiple time delays. A new sufficient condition guaranteeing the uniqueness and global exponential stability of the equilibrium point is established. The new stability criterion imposes constraints, expressed by a linear matrix inequality, on the self-feedback connection matrix and interconnection matrices independent of the time delays. The stability criterion is compared with some existing results, and it is found to be less conservative than existing ones.