Global exponential stability of delayed Hopfield neural networks
Neural Networks
Global convergence rate of recurrently connected neural networks
Neural Computation
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Neural Computation
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Neural Processing Letters
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Neural Processing Letters
Dynamical Behaviors of a Large Class of General Delayed Neural Networks
Neural Computation
Journal of Computational and Applied Mathematics
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
On the Domain Attraction of Fuzzy Neural Networks
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
A domain attraction criterion for interval fuzzy neural networks
Computers & Mathematics with Applications
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Efficient continuous-time asymmetric hopfield networks for memory retrieval
Neural Computation
Convergence analysis of continuous-time neural networks
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
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In this paper, we discuss dynamical behaviors of recurrently asymmetrically connected neural networks in detail. We propose an effective approach to study global and local stability of the networks. Many of well known existing results are unified in our framework, which gives much better test conditions for global and local stability. Sufficient conditions for the uniqueness of the equilibrium point and its stability conditions are given, too