Global attractivity in delayed Hopfield neural network models
SIAM Journal on Applied Mathematics
On the stability analysis of delayed neural networks systems
Neural Networks
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
On global asymptotic stability of recurrent neural networks with time-varying delays
Applied Mathematics and Computation
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
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
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
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Some sufficient conditions for the globally exponential stability of the equilibrium point of neural networks with multiple time varying delays are developed, and the estimation of the exponential convergence rate is presented. The obtained criteria are dependent on time delay, and consist of all the information on the neural networks. The effects of time delay and number of connection matrices of the neural networks on the exponential convergence rate are analyzed, which can give a clear insight into the relation between the exponential convergence rate and the parameters of the neural networks. Two numerical examples are used to demonstrate the effectiveness of the obtained the results.