Antisymmetrical neural networks
Discrete Applied Mathematics
Asymmetric Hopfield-type networks: theory and applications
Neural Networks
Retrieval Properties of a Hopfield Model with Random Asymmetric Interactions
Neural Computation
Stability Analysis of Discrete Hopfield Neural Networks with Weight Function Matrix
ISICA '08 Proceedings of the 3rd International Symposium on Advances in Computation and Intelligence
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Stability conditions for discrete neural networks in partial simultaneous updating mode
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
New stability conditions for Hopfield networks in partial simultaneous update mode
IEEE Transactions on Neural Networks
Stability of asymmetric Hopfield networks
IEEE Transactions on Neural Networks
Exponential stability and periodic oscillatory solution in BAM networks with delays
IEEE Transactions on Neural Networks
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Stability and periodicity of neural networks is important behavior in biological and cognitive activities. In order to better simulate a biological genuine model, a special kind of discrete Hopfield neural networks (SDHNNs) in which every neuron has only one input is considered. By applying permutation theory and mathematical induction, we prove that the SDHNN always converges to a stable state or a limit cycle. The SDHNN is extended to the discrete Hopfield neural networks with column arbitrary-magnitude-dominant weight matrix (DHNNCAMDWM) in which there only exits a magnitude-dominant element in every column. Some important results, especially the periodic stability of the DHNNCAMDWM, are obtained. And the XOR problem is successfully solved by the results.