A Symmetric Probabilistic Encryption Scheme Based On CHNN Without Data Expansion
Neural Processing Letters
Secure Media Distribution Scheme Based on Chaotic Neural Network
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Letters: A block cipher based on chaotic neural networks
Neurocomputing
Traceable content protection based on chaos and neural networks
Applied Soft Computing
A novel image encryption/decryption scheme based on chaotic neural networks
Engineering Applications of Artificial Intelligence
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We first present a modified Hopfield network, the clipped Hopfield network, with synaptic weights assigned to three values {-1,0,+1}. We give the necessary conditions under which a set of 2n binary vectors can be stored as stable points of the network. We show that in the parallel updating mode, for most of the state vectors, the network will always converge to these 2n stable points. We further demonstrate that these 2n stable points can be divided into two groups, the α group and the β group, each with n stable points. It is shown that the basins of attraction of the stable points in the α group are evenly distributed, and the basins of attraction of the stable points in the β group are also evenly distributed. By ways of application, we show that this class of Hopfield network can be used to build a cryptographically secure keystream generator