Antisymmetrical neural networks
Discrete Applied Mathematics
New stability conditions for Hopfield networks in partial simultaneous update mode
IEEE Transactions on Neural Networks
Convergence of discrete delayed Hopfield neural networks
Computers & Mathematics with Applications
Stability conditions for discrete hopfield neural networks with delay
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
Convergence analysis of discrete delayed hopfield neural networks
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Convergence study of discrete neural networks with delay
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
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The stability of recurrent neural networks is known to be bases of successful applications of the networks. Discrete Hopfield neural networks with delay are extension of discrete Hopfield neural networks without delay. In this paper, the stability of discrete Hopfield neural networks with delay is mainly investigated. The method, which does not make use of energy function, is simple and valid for the dynamic behavior analysis of the neural networks with delay. Several new sufficient conditions for the networks with delay converging towards a limit cycle with length 2 are obtained. All results established here generalize the existing results on the stability of both discrete Hopfield neural networks without delay and with delay in parallel updating mode.