Robust dynamic sliding-mode control using adaptive RENN for magnetic levitation system
IEEE Transactions on Neural Networks
Effect of refractoriness on learning performance of a pattern sequence
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A robust extended Elman backpropagation algorithm
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
On the weight convergence of Elman networks
IEEE Transactions on Neural Networks
Quantized Neural Modeling: Hybrid Quantized Architecture in Elman Networks
Neural Processing Letters
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A linear model of recurrent neural networks, called the Elman networks, is combined with the simple nonlinear modulo (mod) operation on its linear activated function so as to generate chaos purposely. Conditions on the weight matrix are obtained, under which the generated chaos satisfies the mathematical definition of chaos in the sense of T.Y. Li and J.A. Yorke (1975). Some simple and representative weight matrices are constructed for designing such Elman networks that can generate Li-Yorke chaos. Several numerical simulations are shown to verify and visualize the design.