Global exponential stability of delayed Hopfield neural networks
Neural Networks
Global exponential stability of competitive neural networks with different time scales
IEEE Transactions on Neural Networks
Stability analysis of bidirectional associative memory networks with time delays
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Passivity Analysis of Dynamic Neural Networks with Different Time-scales
Neural Processing Letters
Computers & Mathematics with Applications
Dynamic associative memory, based on open recurrent neural network
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
IEEE Transactions on Neural Networks
Passivity analysis for neuro identifier with different time-scales
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Dynamics of competitive neural networks with inverse lipschitz neuron activations
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
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A competitive neural network model was recently proposed to describe the dynamics of cortical maps, where there are two types of memories: long-term and short-term memories. Such a network is characterized by a system of differential equations with two types of variables, one models the fast neural activity and the other models the slow modification of synaptic strength. In this paper, we introduce a time delay parameter into the neural network model to characterize the signal transmission delays in real neural systems and the finite switch speed in the circuit implementations of neural networks. Then, we analyze the global exponential stability of the delayed competitive neural networks with different time scales. We allow the model has non-differentiable and unbounded functions, and use the nonsmooth analysis techniques to prove the existence and uniqueness of the equilibrium, and derive a new sufficient condition ensuring global exponential stability of the networks.